The future of digital health with federated learning — TEI-tagged view

Nicola Rieke, Jonny Hancox, Wenqi Li, Fausto Milletarì, Holger R. Roth, Shadi Albarqouni, Spyridon Bakas, Mathieu N. Galtier, Bennett A. Landman, Klaus Maier-Hein, Sébastien Ourselin, Micah Sheller, Ronald M. Summers, Andrew Trask, Daguang Xu, Maximilian Baust, M. Jorge Cardoso · 2020 · machine-learning · doi:10.1038/s41746-020-00323-1 · TEI xml · tagged edition · index · concepts · authors
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Other fields define these differently the same word, defined another way in another discipline
privacy 3 fields · compensation 2 fields
Tags on this paper every tag the record carries, whether or not the words occur in the text below — a number is how many papers in the corpus carry that tag; a tag with no number is unique to this paper
Federated learning3 data governance consensus model anonymisation reidentification privacy8 precision medicine3 license restrictions egress business value firewalls Data-driven machine learning (ML) accurate and robust statistical models data silos privacy concerns without access to sufficient data full potential transition from research to clinical practice Centralised Data Lake FL aggregation server federation of training nodes global model2 partially trained models central server FL peer to peer Centralised training local, independent training ML from non-co-located data data controller governance processes privacy policies data access validation phase large-scale, ininstitutional validation rare diseases3 high-dimensional, storage-intense medical data centralised pool growing global data set data storage requirements FL topologies Centralised Decentralised Hierarchical FL compute plans Sequential training/cyclic transfer learning memorise information differential privacy2 learning from encrypted data volume-based fee-for-service-based value-based healthcare2 focused treatment accuracy and robustness of healthcare AI costs3 patient outcomes3 FL learning paradigm multiple parties global loss function weighted combination of K local losses private data data heterogeneity inhomogeneous data distribution independently and identically distributed (IID) data FedAvg FedProx part-data-sharing strategy FL with domainadaptation global optimal solution optimal for an individual local participant model training optimality Privacy-preserving techniques for FL levels of protection accuracy of the final model future techniques ancillary data2 low-risk Non-trusted security strategies advanced encryption of model submissions secure authentication of all parties traceability of actions verification systems execution integrity model confidentiality protections against adversarial attacks traditional training training process is exposed to multiple parties risk of leakage via reverseengineering multiparty computations training configurations hyperparameter tuning contribution from each participant computational resources consumed quality of the data used for local training compensation7 revenue model secure intra-node viewing facility explainability and interpretability of the global model redundant nodes secure encryption methods data leakage node schedulers “honest broker” system trusted third party peer-topeer system gatekeeper function trustlessbased architecture secure protocol computational overheads review319
Method, as described: a perspective paper: no new experiment, performance claims cited from prior work

Our tag layer for this paper

Cross-disciplinary analysis
content-tags & facets: phenomenon · claim-type · cross-domain-transfer · mechanism · claim-relation · replication-status · limitation — grounded in the full text
phenomenonmedical data sitting in silos that machine learning cannot reach— “Another reason why data sharing is not systematic in healthcare is that collecting, curating, and maintaining a high-quality data set takes considerable time, effort, and expense. Consequently such data sets may have significant business value, making it less likely that they will be freely shared.”
claim-typeprediction— “Not all technical questions have been answered yet and FL will certainly be an active research area throughout the next decade”
method — evidence— “Recent research has shown that models trained by FL can achieve performance levels comparable to ones trained on centrally hosted data sets and superior to models that only see isolated single-institutional data16,17.”
object of study — evidence— “Federated learning (FL)9–11 is a learning paradigm seeking to address the problem of data governance and privacy by training algorithms collaboratively without exchanging the data itself.”
research question — evidence— “By enabling multiple parties to train collaboratively without the need to exchange or centralise data sets, FL neatly addresses issues related to egress of sensitive medical data.”
contribution — evidence— “However, FL still requires rigorous technical consideration to ensure that the algorithm is proceeding optimally without compromising safety or patient privacy.”
mechanismMOVE THE MODEL, NOT THE DATA: training happens locally inside each institution and only model characteristics leave the firewall, so the consensus model is built without the patient records ever being pooled— “FL enables gaining insights collaboratively, e.g., in the form of a consensus model, without moving patient data beyond the firewalls of the institutions in which they reside. Instead, the ML process occurs locally at each participating institution and only model characteristics (e.g., parameters, gradients) are transferred as depicted in…mechanisms[0]
mechanismWHY ANONYMISATION CANNOT BE THE ANSWER: identity survives the removal of the obvious fields - a face is reconstructable from a CT scan, and a handful of record elements permits reidentification— “Even if data anonymisation could bypass these limitations, it is now well understood that removing metadata such as patient name or date of birth is often not enough to preserve privacy7.”mechanisms[1]
mechanismDATA IS WITHHELD FOR COMMERCIAL REASONS AS WELL AS LEGAL ONES: curating a high-quality set costs time and money, which gives it business value and makes sharing less likely - a motive no privacy technology addresses— “Another reason why data sharing is not systematic in healthcare is that collecting, curating, and maintaining a high-quality data set takes considerable time, effort, and expense. Consequently such data sets may have significant business value, making it less likely that they will be freely shared.”mechanisms[2]
senseanonymisation - TREATED HERE AS INSUFFICIENT BY CONSTRUCTION, not as imperfect in practice: removing name and date of birth does not preserve privacy, a face can be reconstructed from a CT scan, and genomic data is as unique as a fingerprint— “Even if data anonymisation could bypass these limitations, it is now well understood that removing metadata such as patient name or date of birth is often not enough to preserve privacy7.”senses[0]
senseFederated learning - collaborative model training without exchanging or centralising the parties' data sets— “Federated learning definition FL is a learning paradigm in which multiple parties train collaboratively without the need to exchange or centralise data sets. A general formulation of FL reads as follows: Let L denote a global loss function obtained via a weighted combination of K local losses fLkgK k¼1, computed from private data Xk, which is…senses[1]
senseAggregation-server FL - nodes receive a global model, train locally, and return partial models for central aggregation— “Fig. 1 Example federated learning (FL) workflows and difference to learning on a Centralised Data Lake. a FL aggregation server—the typical FL workflow in which a federation of training nodes receive the global model, resubmit their partially trained models to a central server intermittently for aggregation and then continue training on the…senses[2]
sensePeer-to-peer FL - each node exchanges partial models with peers and performs its own aggregation— “Fig. 1 Example federated learning (FL) workflows and difference to learning on a Centralised Data Lake. a FL aggregation server—the typical FL workflow in which a federation of training nodes receive the global model, resubmit their partially trained models…senses[3]
senseCentralised training - sites donate data to a shared lake for extraction and independent local trainingsenses[4]
senseFL topology - centralised, decentralised, or hierarchical communication architecture
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senses[5]
senseFL compute plan - trajectory followed by a model across participating partners
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senses[6]
senseData heterogeneity - non-IID cross-institutional distributions that challenge federated aggregation and local optimality
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senses[7]
sensePrivacy-performance trade-off - stronger federated protections may affect final-model accuracy
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senses[8]
senseNon-trusted federation - collaboration needing encryption, authentication, traceability, verification, integrity, and adversarial defenses
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senses[9]
senseFederated information leakage - model changes exposed to multiple parties create reverse-engineering risk even without raw-data sharing
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senses[10]
senseHonest-broker administration - trusted intermediary controls the federation and abstracts internal mechanisms from clients
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senses[11]
sensePeer-to-peer administration - sites interact directly with no gatekeeper and must synchronise protocol changes
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senses[12]
auditFederated learning —distinct-from→ anonymisation — Audit 2026-09-28: DOUBTFUL - read against its span and kept, but the span describes federated learning's privacy aim; distinct-from 'anonymisation' is loose.
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concept_edges[0]
weightFederated learning —distinct-from→ anonymisation — 0.8
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effect directionwithout access to sufficient data —causes→ transition from research to clinical practice — eliminates
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concept_edges[1]
notewithout access to sufficient data —causes→ transition from research to clinical practice — The paper says insufficient access prevents ML from making the research-to-clinic transition.
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concept_edges[1]
noteCentralised training —distinct-from→ FL aggregation server — The figure explicitly presents the difference between federated workflows and the non-FL centralised-data-lake workflow.
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concept_edges[2]
auditlearning from encrypted data —causes→ privacy — Audit 2026-09-28: DOUBTFUL - read against its full span and kept, but the span says learning from encrypted data HAS BEEN PROPOSED to further enhance privacy; hedged.
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concept_edges[3]
effect directionlearning from encrypted data —causes→ privacy — increases
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evidence status notelearning from encrypted data —causes→ privacy — THE QUOTE CONTINUES A SENTENCE THAT THIS COPY CUTS IN TWO - a figure caption (the FL compute plans panels) stands between it and the words it follows, which are not adjacent to it in the extraction.
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concept_edges[3]
notelearning from encrypted data —causes→ privacy — The same proposed-mechanism hedge applies, so paper weight is 0.7 while confidence remains 0.95.
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concept_edges[3]
weightlearning from encrypted data —causes→ privacy — 0.7
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concept_edges[3]
noteFL —is-a→ learning paradigm — The paper directly defines FL as a learning paradigm.
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concept_edges[4]
auditglobal optimal solution —distinct-from→ optimal for an individual local participant — Audit 2026-09-28: DOUBTFUL - read against its full span and kept, but the span says the global optimum MAY not be optimal for a local participant; hedged.
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concept_edges[5]
noteglobal optimal solution —distinct-from→ optimal for an individual local participant — The paper says the global optimum may not be locally optimal; weight 0.7 preserves both modal hedges.
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concept_edges[5]
weightglobal optimal solution —distinct-from→ optimal for an individual local participant — 0.7
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effect directiontraining process is exposed to multiple parties —causes→ risk of leakage via reverseengineering — increases
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concept_edges[6]
notetraining process is exposed to multiple parties —causes→ risk of leakage via reverseengineering — The paper directly says multi-party exposure increases reverse-engineering leakage risk.
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concept_edges[6]
auditsecure encryption methods —causes→ data leakage — Audit 2026-09-28: DOUBTFUL - read against its full span and kept, but the span names designing secure encryption methods to prevent leakage as a CHALLENGE; causes is loose.
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effect directionsecure encryption methods —causes→ data leakage — reduces
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concept_edges[7]
notesecure encryption methods —causes→ data leakage — Secure encryption is named as an architecture measure to prevent data leakage.
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auditFL —causes→ accuracy and robustness of healthcare AI — Audit 2026-09-28: DOUBTFUL - read against its full span and kept, but the span says FL HAS THE POTENTIAL to increase accuracy and robustness; hedged.
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effect directionFL —causes→ accuracy and robustness of healthcare AI — increases
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concept_edges[8]
noteFL —causes→ accuracy and robustness of healthcare AI — Weight 0.7 preserves the paper's 'has the potential to increase' wording.
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concept_edges[8]
weightFL —causes→ accuracy and robustness of healthcare AI — 0.7
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auditFL —causes→ costs — Audit 2026-09-28: DOUBTFUL - read against its span and kept, but the span shown is about paradigm shifts in healthcare payment; the cost claim is not visible.
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effect directionFL —causes→ costs — reduces
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concept_edges[9]
noteFL —causes→ costs — The projected cost reduction shares the paper's potential-language hedge.
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concept_edges[9]
weightFL —causes→ costs — 0.7
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concept_edges[9]
auditFL —causes→ patient outcomes — Audit 2026-09-28: DOUBTFUL - read against its full span and kept, but the span says FL HAS THE POTENTIAL to improve patient outcomes; hedged.
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concept_edges[10]
effect directionFL —causes→ patient outcomes — increases
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concept_edges[10]
noteFL —causes→ patient outcomes — The projected improvement in patient outcomes is weighted 0.7 for the paper's modal framing.
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weightFL —causes→ patient outcomes — 0.7
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concept_edges[10]
evidence status noteconcept — THE QUOTE CONTINUES A SENTENCE THAT THIS COPY CUTS IN TWO - a figure caption (the FL compute plans panels) stands between it and the words it follows, which are not adjacent to it in the extraction.
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concepts[45]
evidence status noteconcept — THE QUOTE CONTINUES A SENTENCE THAT THIS COPY CUTS IN TWO - a figure caption (the FL compute plans panels) stands between it and the words it follows, which are not adjacent to it in the extraction.
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concepts[46]
evidence status noteconcept — THE QUOTE CONTINUES A SENTENCE THAT THIS COPY CUTS IN TWO - a figure caption (the FL compute plans panels) stands between it and the words it follows, which are not adjacent to it in the extraction.
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concepts[47]
no type for thisFL → Federated learning — FL expands to Federated learning. Acronym expansion is absent from the eight edge types.
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unhoused_relations[0]
no type for thisFL → ML from non-co-located data — FL enables machine learning from non-co-located data. Enablement is absent from the eight edge types and is not safely reducible to causation here.
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unhoused_relations[1]
no type for thisNon-trusted → security strategies — Non-trusted FL collaborations require security strategies. Prerequisite-for is absent from the eight edge types.
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unhoused_relations[2]
no type for thistraceability and accountability processes → execution integrity — Traceability and accountability in non-trusted federations require execution integrity. Prerequisite-for is absent from the eight edge types.
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unhoused_relations[3]
no type for this“honest broker” system → trusted third party — An honest-broker federation is administered through a trusted third party acting as intermediary. Administered-through or mediated-by is absent from the eight edge types.
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unhoused_relations[4]
limitationFL is stated to still require rigorous technical consideration to avoid compromising safety or patient privacy - the paradigm is offered as promising rather than solved
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limitations[0]
limitationThe authors say plainly that not all technical questions have been answered and expect FL to remain an open research area for a decade
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limitations[1]
limitationThe performance claim is attributed to other work rather than demonstrated here - this is a perspective paper citing comparisons, not running them
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limitations[2]
limitationPublic medical data carries varying license restrictions that sometimes limit its exploitation, which bounds what any centralised alternative could have used anyway
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limitations[3]
Concepts
terms this paper carries, each with the sentence it was cut from — the layer that joins one paper to another. Terms with no quote yet are on CONCEPTS.html.
conceptFederated learning
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concepts[0]
conceptdata governance
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concepts[1]
conceptconsensus model
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[2]
conceptanonymisation
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[3]
conceptreidentification
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[4]
conceptprivacy
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[5]
conceptprecision medicine
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concepts[6]
conceptlicense restrictions
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concepts[7]
conceptegress
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[8]
conceptbusiness value
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[9]
conceptfirewalls
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[10]
conceptData-driven machine learning (ML) — not defined in this paper's own words; medical-model development from large healthcare data volumes
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[11]
conceptaccurate and robust statistical models — not defined in this paper's own words; target models built from medical data
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[12]
conceptdata silos — not defined in this paper's own words; institutional separation that limits exploitation of medical data
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[13]
conceptprivacy concerns — not defined in this paper's own words; second barrier restricting access to medical data
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concepts[14]
conceptwithout access to sufficient data — not defined in this paper's own words; data-access condition that blocks ML deployment
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concepts[15]
conceptfull potential — not defined in this paper's own words; capability ML cannot reach without sufficient data
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concepts[16]
concepttransition from research to clinical practice — not defined in this paper's own words; deployment transition blocked by insufficient data
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concepts[17]
conceptCentralised Data Lake — not defined in this paper's own words; pooled-data alternative contrasted with federated workflows
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concepts[18]
conceptFL aggregation server — not defined in this paper's own words; federated workflow coordinated by a central aggregation server
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concepts[19]
conceptfederation of training nodes — not defined in this paper's own words; participants receiving and returning partially trained models
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concepts[20]
conceptglobal model — not defined in this paper's own words; model distributed to training nodes
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concepts[21]
conceptpartially trained models — not defined in this paper's own words; local model states resubmitted for aggregation
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concepts[22]
conceptcentral server — not defined in this paper's own words; coordinator that aggregates participant models
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concepts[23]
conceptFL peer to peer — not defined in this paper's own words; alternative workflow with local exchange and aggregation
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[24]
conceptCentralised training — not defined in this paper's own words; non-federated workflow donating data to a central lake
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[25]
conceptlocal, independent training — not defined in this paper's own words; training performed after extracting data from a central lake
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[26]
conceptML from non-co-located data — not defined in this paper's own words; core federated promise for privacy and governance
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[27]
conceptdata controller — not defined in this paper's own words; party defining governance, privacy, and access policy
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concepts[28]
conceptgovernance processes — not defined in this paper's own words; locally defined rules for federated participation
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[29]
conceptprivacy policies — not defined in this paper's own words; locally defined privacy constraints
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[30]
conceptdata access — not defined in this paper's own words; controller capability that can be revoked
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[31]
conceptvalidation phase — not defined in this paper's own words; phase covered by local access control
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[32]
conceptlarge-scale, ininstitutional validation — not defined in this paper's own words; validation opportunity enabled without pooling data
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[33]
conceptrare diseases — not defined in this paper's own words; research setting where each institution has too little data
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[34]
concepthigh-dimensional, storage-intense medical data — not defined in this paper's own words; data kept at local institutions rather than duplicated
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[35]
conceptcentralised pool — not defined in this paper's own words; storage-intensive pooling arrangement avoided by FL
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[36]
conceptgrowing global data set — not defined in this paper's own words; distributed data collection with which the model can scale
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[37]
conceptdata storage requirements — not defined in this paper's own words; resource demand not increased disproportionately by model-to-data transfer
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[38]
conceptFL topologies — not defined in this paper's own words; communication architectures of a federation
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[39]
conceptCentralised — not defined in this paper's own words; topology coordinated by an aggregation server
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[40]
conceptDecentralised — not defined in this paper's own words; topology with peer connections and local parallel aggregation
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[41]
conceptHierarchical — not defined in this paper's own words; topology composed from mixed sub-federations
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[42]
conceptFL compute plans — not defined in this paper's own words; model trajectories across several partners
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[43]
conceptSequential training/cyclic transfer learning — not defined in this paper's own words; compute plan moving a model sequentially
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[44]
conceptmemorise information — not defined in this paper's own words; privacy risk carried by trained models under some conditions
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[45]
conceptdifferential privacy — not defined in this paper's own words; mechanism proposed to enhance federated privacy
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[46]
conceptlearning from encrypted data — not defined in this paper's own words; second privacy-enhancing mechanism proposed for FL
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[47]
conceptvolume-based — not defined in this paper's own words; fee-for-service healthcare model
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[48]
conceptfee-for-service-based — not defined in this paper's own words; expansion of volume-based healthcare
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[49]
conceptvalue-based healthcare — not defined in this paper's own words; outcomes-focused model linked to precision medicine
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[50]
conceptfocused treatment — not defined in this paper's own words; route to earlier outcomes and lower cost
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[51]
conceptaccuracy and robustness of healthcare AI — not defined in this paper's own words; model qualities FL may increase
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[52]
conceptcosts — not defined in this paper's own words; resource burden FL may reduce
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[53]
conceptpatient outcomes — not defined in this paper's own words; clinical result FL may improve
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[54]
conceptFL — not defined in this paper's own words; abbreviation used for federated learning
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[55]
conceptlearning paradigm — not defined in this paper's own words; class assigned to federated learning
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[56]
conceptmultiple parties — not defined in this paper's own words; collaborators training without centralising their data
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[57]
conceptglobal loss function — not defined in this paper's own words; weighted combination of local losses
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[58]
conceptweighted combination of K local losses — not defined in this paper's own words; general mathematical formulation of global loss
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[59]
conceptprivate data — not defined in this paper's own words; party-resident input never shared among collaborators
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[60]
conceptdata heterogeneity — not defined in this paper's own words; cross-site diversity creating federated optimisation challenges
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[61]
conceptinhomogeneous data distribution — not defined in this paper's own words; non-IID distribution challenging FL algorithms
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[62]
conceptindependently and identically distributed (IID) data — not defined in this paper's own words; assumption violated by many medical federations
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[63]
conceptFedAvg — not defined in this paper's own words; aggregation strategy prone to failure under inhomogeneous distributions
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[64]
conceptFedProx — not defined in this paper's own words; strategy addressing heterogeneous federated data
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[65]
conceptpart-data-sharing strategy — not defined in this paper's own words; second strategy addressing heterogeneous data
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[66]
conceptFL with domainadaptation — not defined in this paper's own words; third strategy addressing heterogeneous data
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[67]
conceptglobal optimal solution — not defined in this paper's own words; federation-wide optimum that may not serve each participant
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[68]
conceptoptimal for an individual local participant — not defined in this paper's own words; local optimality contrasted with the global solution
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[69]
conceptmodel training optimality — not defined in this paper's own words; criterion participants should agree before training
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[70]
conceptPrivacy-preserving techniques for FL — not defined in this paper's own words; protections exceeding contemporary commercial ML models
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[71]
conceptlevels of protection — not defined in this paper's own words; privacy benefit offered by protection techniques
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[72]
conceptaccuracy of the final model — not defined in this paper's own words; performance dimension privacy techniques may affect
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[73]
conceptfuture techniques — not defined in this paper's own words; future capabilities that could compromise a low-risk model
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[74]
conceptancillary data — not defined in this paper's own words; additional data that could compromise a low-risk model
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[75]
conceptlow-risk — not defined in this paper's own words; prior model risk assessment vulnerable to later compromise
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[76]
conceptNon-trusted — not defined in this paper's own words; large-scale collaboration without enforceable trust agreements
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[77]
conceptsecurity strategies — not defined in this paper's own words; countermeasures required in non-trusted federations
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[78]
conceptadvanced encryption of model submissions — not defined in this paper's own words; non-trusted federation security strategy
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[79]
conceptsecure authentication of all parties — not defined in this paper's own words; identity security strategy
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[80]
concepttraceability of actions — not defined in this paper's own words; accountability strategy for non-trusted federation
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[81]
conceptverification systems — not defined in this paper's own words; integrity strategy for non-trusted federation
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[82]
conceptexecution integrity — not defined in this paper's own words; required property of accountable non-trusted processing
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[83]
conceptmodel confidentiality — not defined in this paper's own words; protected model property
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[84]
conceptprotections against adversarial attacks — not defined in this paper's own words; security strategy for malicious clients
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[85]
concepttraditional training — not defined in this paper's own words; training mode contrasted with multi-party exposure
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[86]
concepttraining process is exposed to multiple parties — not defined in this paper's own words; federated exposure increasing reverse-engineering leakage risk
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[87]
conceptrisk of leakage via reverseengineering — not defined in this paper's own words; privacy risk increased by observing federated model changes
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[88]
conceptmultiparty computations — not defined in this paper's own words; distributed computations requiring traceable configurations
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[89]
concepttraining configurations — not defined in this paper's own words; configuration state that must be traced
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[90]
concepthyperparameter tuning — not defined in this paper's own words; training activity requiring traceability
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[91]
conceptcontribution from each participant — not defined in this paper's own words; measured input used for compensation and revenue models
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[92]
conceptcomputational resources consumed — not defined in this paper's own words; one participant-contribution measure
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[93]
conceptquality of the data used for local training — not defined in this paper's own words; second participant-contribution measure
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[94]
conceptcompensation — not defined in this paper's own words; allocation determined from participant contribution
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[95]
conceptrevenue model — not defined in this paper's own words; federation economics based on participant measurements
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[96]
conceptsecure intra-node viewing facility — not defined in this paper's own words; controlled way to inspect local data
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[97]
conceptexplainability and interpretability of the global model — not defined in this paper's own words; goal of controlled data-viewing support
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[98]
conceptredundant nodes — not defined in this paper's own words; architecture measure for communication data integrity
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[99]
conceptsecure encryption methods — not defined in this paper's own words; architecture measure preventing data leakage
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[100]
conceptdata leakage — not defined in this paper's own words; risk targeted by secure encryption
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[101]
conceptnode schedulers — not defined in this paper's own words; resource-allocation mechanism reducing idle time
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[102]
concept“honest broker” system — not defined in this paper's own words; federation administered through a trusted intermediary
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[103]
concepttrusted third party — not defined in this paper's own words; intermediary controlling an honest-broker system
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[104]
conceptpeer-topeer system — not defined in this paper's own words; federation in which sites interact directly
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[105]
conceptgatekeeper function — not defined in this paper's own words; central control absent from peer-to-peer administration
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[106]
concepttrustlessbased architecture — not defined in this paper's own words; architecture cryptographically constraining the operator
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[107]
conceptsecure protocol — not defined in this paper's own words; cryptographic means of locking the operator into honest behavior
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[108]
conceptcomputational overheads — not defined in this paper's own words; cost introduced by trustless protocols
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[109]

When we have changed our mind. Some entries in the table begin with a word in capitals and the reason for it. EVIDENCE CORRECTED — the finding still stands, but we had quoted the wrong sentence from the paper, and the quotation has been replaced with the right one. RETRACTED — the entry itself was wrong; it stays on the page, marked, with the reason, rather than being deleted. WITHDRAWN AND REPLACED — the entry was wrong, and a corrected version of it appears in the entries below. Each mark carries its own reason, in brackets, because the reasons differ from one entry to the next. An entry we have WITHDRAWN is removed from this page and kept in the record, with its reason. A line reading Audit and a date is a later re-reading of the entry against its whole sentence: DOUBTFUL means it was kept, and the reason says what is loose about it. Quotation marks inside the tagger's sentence. Where a tag is the tagger's own sentence, a phrase in quotation marks inside it is borrowed from the paper — the tagger's wording around it is not. The paper's own sentence is the evidence quote for that row; on this page it is withheld with the article, and the article is at its publisher. Nothing inside quotation marks anywhere on this page is ours: if it is quoted, it is the paper's. Whose words a concept carries. A concept entry records a term the paper itself uses, so the term, the sense given for it, and its evidence are the paper's own words, quoted where the paper fixes them. This is the opposite of a sense entry in the tag table, which is the tagger's sentence about how the paper uses a term. Where a concept entry could not be grounded in the paper's words, the entry says so on its own row.

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