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Federated learning

Federated learning (also known as collaborative learning) is a machine learning technique in a setting where multiple entities (often called clients) collaboratively train a model while keeping their data decentralized, rather than centrally stored. A defining characteristic of federated learning is data heterogeneity. Because client data is…

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Overview

Definition

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Algorithmic hyper-parameters

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Map overview Semantic statistics

Federated learning

Nodes68
Edges67
Triples159
Avg. degree1.97
Density0.029412
Components1

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Federated learning

Top relations

related to Medicine: digital health · 22
Federated learning → AI, Applications, COVID-19, Data Properties, Digital Health, Federated, FL, From, Furthermore, Healthcare Area, In, MedPerf, Nature Digital Medicine, Nature Medicine, Perspective, Recently, September, Systematic Review, The, The Future
related to Federated learning with dynamic regularization (FedDyn) · 12
Federated learning → Acar, Besides, FedDyn, FedDynOneGD, Federated, Hence, In, Moreover, SGD, Since, These, To
related to Current research topics · 11
Federated learning → Another, Before, Developing, DNN, Federated, Framework, In, Multi-source Prefetching Through Adaptive, Other, Recent, Weight
related to Hybrid federated dual coordinate ascent (HyFDCA) · 10
Federated learning → Ascent, CoCoA, FL, Hybrid Federated Dual Coordinate, HyFDCA, Jaggi, Smith, This, Very, Yet
related to Governance · 8
Federated learning → As, Establishing, Governance, In, Most, Such, These, This
related to Personalized federated learning by pruning (Sub-FedAvg) · 8
Federated learning → Do, Federated, If, IID, Sub-FedAvg, This, To, Vahidian
related to Definition · 7
Federated learning → Federated, IoT, Moreover, None, The, While, Wi-Fi
related to Industry 4.0: smart manufacturing · 7
Federated learning → Federated, FL, In, In Industry, Nevertheless, PM2, Smart
related to Non-IID data · 7
Federated learning → IID, In, Peter Kairouz, The, This, To, Under
related to Personalized federated learning · 7
Federated learning → Clients, Heterogeneous, In, IoT, Personalized, PFL, The

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Important terminology

learning federated data local model nodes training models may global server different datasets node machine also updates clients hyfdca process

Entity relationships Subject–Predicate–Object triples

SubjectPredicateObjectConfidenceSrc
data privacyinstance ofdata samples held by each client may not be independently and identically distributed.Federated learning is generally concerned with and motivated by issues0.80text
data minimizationinstance ofdata samples held by each client may not be independently and identically distributed.Federated learning is generally concerned with and motivated by issues0.80text
and data access rightsinstance ofdata samples held by each client may not be independently and identically distributed.Federated learning is generally concerned with and motivated by issues0.80text
ADAMinstance ofFedAvg variations have been proposed based on adaptive optimizers0.80text
AdaGradinstance ofFedAvg variations have been proposed based on adaptive optimizers0.80text
and tend to outperform FedAvg.Federated Proximalinstance ofFedAvg variations have been proposed based on adaptive optimizers0.80text
and tend to outperform FedAvginstance ofFedAvg variations have been proposed based on adaptive optimizers0.80text
facialinstance ofmaking it particularly effective for diverse biometric applications0.80text
iris recognitioninstance ofmaking it particularly effective for diverse biometric applications0.80text
Federated learningrelated to BiometricsFL0.60section
Federated learningrelated to BiometricsBy0.60section
Federated learningrelated to BiometricsIt0.60section

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