Clustered Federated Learning: Model-Agnostic Distributed Multitask Optimization Under Privacy Constraints

Federated learning (FL) is currently the most widely adopted framework for collaborative training of (deep) machine learning models under privacy constraints. Albeit its popularity, it has been observed that FL yields suboptimal results if the local clients' data distributions diverge. To addre...

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Vydáno v:	IEEE transaction on neural networks and learning systems Ročník 32; číslo 8; s. 3710 - 3722
Hlavní autoři:	Sattler, Felix, Muller, Klaus-Robert, Samek, Wojciech
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 01.08.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Artificial neural networks Clients Clustering Constraint modelling Data models distributed learning Federated learning Learning algorithms Machine learning Mathematical models multi-task learning Neural networks Optimization Privacy Recurrent neural networks Servers Sociology Statistics Theoretical analysis Training
ISSN:	2162-237X, 2162-2388, 2162-2388
On-line přístup:	Získat plný text
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