The future of digital health with federated learning

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 · npj Digital Medicine · 2020 · machine-learning · 10.1038/s41746-020-00323-1 · record rieke2020-the-future-of-digital-health-with
tag layer · index · concepts · authors · עברית
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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, each linking to every other paper in the corpus that carries it. A tag appears here whether or not its words occur in the article — the spans below are only the ones a sentence in this paper licenses. Of the 110 concept tags on this paper, all 110 are grounded in a quoted sentence. 99 carry the tagger's gloss of what the term means in this paper, which is not something the article can license.
Federated learning data governance consensus model anonymisation reidentification privacy precision medicine 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 model 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 diseases 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 privacy learning from encrypted data volume-based fee-for-service-based value-based healthcare focused treatment accuracy and robustness of healthcare AI costs patient outcomes 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 data 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 compensation 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 MOVE 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 WHY 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 DATA 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 review
Method, as described: a perspective paper: no new experiment, performance claims cited from prior work

Legend

claim typeclaim_typethe shape of the claim: existence, mechanism, method, critique, prediction, null
concept edgeconcept_edgesan asserted relation between two concepts, with the sentence asserting it
conceptconceptsan idea the paper works with, quoted where the paper uses it
contributioncontributionwhat the paper claims to add
limitationlimitationswhat the paper says its evidence cannot support
mechanismmechanismshow the reported effect is said to come about
methodmethodhow the paper produced its evidence
object of studyobject_of_studywhat the paper is about — the thing it investigates
phenomenonphenomenonthe effect or regularity under study
research questionresearch_questionthe question the paper sets itself
sensesensesa distinct use of a term, quoted where the paper fixes it
unrecordable relationunhoused_relationsa relation the paper asserts that this vocabulary cannot express, with the reason

The paper

The full text is not reproduced here. This article is not cleared for redistribution, so the page carries the tag layer only. Its evidence sentences are withheld; the grounding status remains with each tag, and the article itself is at https://doi.org/10.1038/s41746-020-00323-1. The reading view exists for the papers whose licence permits it.

Every tag on this page

claim_type 1 · concept_edges 11 · concepts 110 · contribution 1 · limitations 4 · mechanisms 3 · method 1 · object_of_study 1 · phenomenon 1 · research_question 1 · senses 13 · unhoused_relations 5

Relations on this page carry a quotation for the claim. Where a relation was separately evidenced — and so tested against the vocabulary for its type — the row says so; 10 of 11 relations here carry that mark. The rest are the tagger’s reading of the sentence quoted beside them.

This record holds no relations between claims or controls. A layer is empty when the tagger recorded nothing in it — which is not the same as the paper having nothing to record.

facettagfieldconf.whose words
object_of_studyfederated learning as an answer to data governance and privacy constraints in healthcare machine learning
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object_of_studythe paper's words
research_questionhow to train on medical data that cannot be centralised
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research_questionthe paper's words
contributiona consensus perspective on what FL offers digital health and what it still requires
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contributionthe paper's words
claim_typeprediction
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claim_typethe paper's words
phenomenonmedical data sitting in silos that machine learning cannot reach
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phenomenonthe paper's words
methodreview
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methodthe paper's words
mechanismsmechanisms
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mechanisms[0]0.95the tagger's words
mechanismsmechanisms
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mechanisms[1]0.95the tagger's words
mechanismsmechanisms
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mechanisms[2]0.95the tagger's words
limitationsFL 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]0.95the tagger's words
limitationsThe 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]0.95the tagger's words
limitationsThe 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]0.95the tagger's words
limitationsPublic 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]0.95the tagger's words
conceptsFederated learning
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concepts[0]0.95the paper's words
conceptsdata governance
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conceptsconsensus model
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conceptsanonymisation
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conceptsreidentification
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conceptsprivacy
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also in: privacy
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conceptsprecision medicine
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concepts[6]0.85the paper's words
conceptslicense restrictions
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conceptsegress
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conceptsbusiness value
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conceptsfirewalls
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conceptsData-driven machine learning (ML)
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conceptsaccurate and robust statistical models
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conceptsdata silos
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concepts[13]0.95the paper's words
conceptsprivacy concerns
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concepts[14]0.95the paper's words
conceptswithout access to sufficient data
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concepts[15]0.95the paper's words
conceptsfull potential
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conceptstransition from research to clinical practice
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conceptsCentralised Data Lake
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conceptsFL aggregation server
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concepts[19]0.95the paper's words
conceptsfederation of training nodes
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conceptsglobal model
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conceptspartially trained models
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conceptscentral server
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conceptsFL peer to peer
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conceptsCentralised training
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conceptslocal, independent training
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conceptsML from non-co-located data
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conceptsdata controller
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conceptsgovernance processes
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conceptsprivacy policies
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conceptsdata access
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conceptsvalidation phase
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conceptslarge-scale, ininstitutional validation
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conceptsrare diseases
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conceptshigh-dimensional, storage-intense medical data
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conceptscentralised pool
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conceptsgrowing global data set
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conceptsdata storage requirements
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conceptsFL topologies
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conceptsCentralised
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conceptsDecentralised
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conceptsHierarchical
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conceptsFL compute plans
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conceptsSequential training/cyclic transfer learning
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conceptsmemorise information
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about this quote: 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.
concepts[45]0.95the paper's words
conceptsdifferential privacy
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about this quote: 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.
concepts[46]0.95the paper's words
conceptslearning from encrypted data
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about this quote: 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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conceptsvolume-based
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conceptsfee-for-service-based
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conceptsvalue-based healthcare
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conceptsfocused treatment
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conceptsaccuracy and robustness of healthcare AI
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conceptscosts
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conceptspatient outcomes
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conceptsFL
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conceptslearning paradigm
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conceptsmultiple parties
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conceptsglobal loss function
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conceptsweighted combination of K local losses
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conceptsprivate data
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conceptsdata heterogeneity
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conceptsinhomogeneous data distribution
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conceptsindependently and identically distributed (IID) data
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conceptsFedAvg
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conceptsFedProx
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conceptspart-data-sharing strategy
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conceptsFL with domainadaptation
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conceptsglobal optimal solution
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conceptsoptimal for an individual local participant
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conceptsmodel training optimality
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conceptsPrivacy-preserving techniques for FL
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conceptslevels of protection
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conceptsaccuracy of the final model
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conceptsfuture techniques
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conceptsancillary data
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conceptslow-risk
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conceptsNon-trusted
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conceptssecurity strategies
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conceptsadvanced encryption of model submissions
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conceptssecure authentication of all parties
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conceptstraceability of actions
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conceptsverification systems
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conceptsexecution integrity
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conceptsmodel confidentiality
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conceptsprotections against adversarial attacks
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conceptstraditional training
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conceptstraining process is exposed to multiple parties
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conceptsrisk of leakage via reverseengineering
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conceptsmultiparty computations
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conceptstraining configurations
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conceptshyperparameter tuning
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conceptscontribution from each participant
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conceptscomputational resources consumed
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conceptsquality of the data used for local training
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conceptscompensation
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conceptsrevenue model
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conceptssecure intra-node viewing facility
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conceptsexplainability and interpretability of the global model
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conceptsredundant nodes
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conceptssecure encryption methods
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conceptsdata leakage
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conceptsnode schedulers
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concepts“honest broker” system
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conceptstrusted third party
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conceptspeer-topeer system
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concepts[105]0.95the paper's words
conceptsgatekeeper function
Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.
concepts[106]0.95the paper's words
conceptstrustlessbased architecture
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concepts[107]0.95the paper's words
conceptssecure protocol
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concepts[108]0.95the paper's words
conceptscomputational overheads
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concepts[109]0.95the paper's words
unhoused_relationsFL → Federated learning
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FL expands to Federated learning. Acronym expansion is absent from the eight edge types.
unhoused_relations[0]0.95the tagger's words
unhoused_relationsFL → ML from non-co-located data
Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.
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.
unhoused_relations[1]0.95the tagger's words
unhoused_relationsNon-trusted → security strategies
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Non-trusted FL collaborations require security strategies. Prerequisite-for is absent from the eight edge types.
unhoused_relations[2]0.95the tagger's words
unhoused_relationstraceability and accountability processes → execution integrity
Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.
Traceability and accountability in non-trusted federations require execution integrity. Prerequisite-for is absent from the eight edge types.
unhoused_relations[3]0.95the tagger's words
unhoused_relations“honest broker” system → trusted third party
Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.
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.
unhoused_relations[4]0.95the tagger's words
sensesanonymisation - 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
Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.
senses[0]0.95the tagger's words
sensesFederated learning - collaborative model training without exchanging or centralising the parties' data sets
Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.
senses[1]0.95the tagger's words
sensesAggregation-server FL - nodes receive a global model, train locally, and return partial models for central aggregation
Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.
senses[2]0.95the tagger's words
sensesPeer-to-peer FL - each node exchanges partial models with peers and performs its own aggregation
Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.
senses[3]0.95the tagger's words
sensesCentralised training - sites donate data to a shared lake for extraction and independent local training
Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.
senses[4]0.95the tagger's words
sensesFL topology - centralised, decentralised, or hierarchical communication architecture
Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.
senses[5]0.95the tagger's words
sensesFL compute plan - trajectory followed by a model across participating partners
Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.
senses[6]0.95the tagger's words
sensesData heterogeneity - non-IID cross-institutional distributions that challenge federated aggregation and local optimality
Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.
senses[7]0.95the tagger's words
sensesPrivacy-performance trade-off - stronger federated protections may affect final-model accuracy
Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.
senses[8]0.95the tagger's words
sensesNon-trusted federation - collaboration needing encryption, authentication, traceability, verification, integrity, and adversarial defenses
Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.
senses[9]0.95the tagger's words
sensesFederated information leakage - model changes exposed to multiple parties create reverse-engineering risk even without raw-data sharing
Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.
senses[10]0.95the tagger's words
sensesHonest-broker administration - trusted intermediary controls the federation and abstracts internal mechanisms from clients
Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.
senses[11]0.95the tagger's words
sensesPeer-to-peer administration - sites interact directly with no gatekeeper and must synchronise protocol changes
Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.
senses[12]0.95the tagger's words
concept_edgesFederated learning —distinct-from→ anonymisation
Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.
strength of the relation: 0.8 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.
concept_edges[0]the tagger's words
concept_edgeswithout access to sufficient data —causes→ transition from research to clinical practice
Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.
direction on the measure: eliminates · the relation itself is separately evidenced · strength of the relation: 1 · note: The paper says insufficient access prevents ML from making the research-to-clinic transition.
concept_edges[1]0.95the tagger's words
concept_edgesCentralised training —distinct-from→ FL aggregation server
Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.
the relation itself is separately evidenced · strength of the relation: 1 · note: The figure explicitly presents the difference between federated workflows and the non-FL centralised-data-lake workflow.
concept_edges[2]0.95the tagger's words
concept_edgeslearning from encrypted data —causes→ privacy
Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.
direction on the measure: increases · the relation itself is separately evidenced · strength of the relation: 0.7 · about this quote: 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. · note: The same proposed-mechanism hedge applies, so paper weight is 0.7 while confidence remains 0.95. 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.
concept_edges[3]0.95the tagger's words
concept_edgesFL —is-a→ learning paradigm
Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.
the relation itself is separately evidenced · strength of the relation: 1 · note: The paper directly defines FL as a learning paradigm.
also in: FL · learning paradigm
concept_edges[4]0.95the tagger's words
concept_edgesglobal optimal solution —distinct-from→ optimal for an individual local participant
Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.
the relation itself is separately evidenced · strength of the relation: 0.7 · note: The paper says the global optimum may not be locally optimal; weight 0.7 preserves both modal hedges. 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.
concept_edges[5]0.95the tagger's words
concept_edgestraining process is exposed to multiple parties —causes→ risk of leakage via reverseengineering
Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.
direction on the measure: increases · the relation itself is separately evidenced · strength of the relation: 1 · note: The paper directly says multi-party exposure increases reverse-engineering leakage risk.
concept_edges[6]0.95the tagger's words
concept_edgessecure encryption methods —causes→ data leakage
Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.
direction on the measure: reduces · the relation itself is separately evidenced · strength of the relation: 1 · note: Secure encryption is named as an architecture measure to prevent 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.
concept_edges[7]0.95the tagger's words
concept_edgesFL —causes→ accuracy and robustness of healthcare AI
Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.
direction on the measure: increases · the relation itself is separately evidenced · strength of the relation: 0.7 · note: Weight 0.7 preserves the paper's 'has the potential to increase' wording. 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.
concept_edges[8]0.95the tagger's words
concept_edgesFL —causes→ costs
Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.
direction on the measure: reduces · the relation itself is separately evidenced · strength of the relation: 0.7 · note: The projected cost reduction shares the paper's potential-language hedge. 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.
also in: FL · costs
concept_edges[9]0.95the tagger's words
concept_edgesFL —causes→ patient outcomes
Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.
direction on the measure: increases · the relation itself is separately evidenced · strength of the relation: 0.7 · note: The projected improvement in patient outcomes is weighted 0.7 for the paper's modal framing. 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.
also in: FL · patient outcomes
concept_edges[10]0.95the tagger's words

Interpretive choices

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© 2026 Shir Sivroni · ORCID 0009-0000-2597-2824