Find Your Friends: Personalized Federated Learning with the Right Collaborators

In the traditional federated learning setting, a central server coordinates a network of clients to train one global model. However, the global model may serve many clients poorly due to data heterogeneity. Moreover, there may not exist a trusted central party that can coordinate the clients to ensu...

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Bibliographic Details
Published in:arXiv.org 2022-10
Main Authors: Sui, Yi, Wen, Junfeng, Lau, Yenson, Ross, Brendan Leigh, Cresswell, Jesse C
Format: Article
Language:English
Subjects:
Algorithms
Clients
Customization
Heterogeneity
Machine learning
Optimization
Online Access:Get full text
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Summary:In the traditional federated learning setting, a central server coordinates a network of clients to train one global model. However, the global model may serve many clients poorly due to data heterogeneity. Moreover, there may not exist a trusted central party that can coordinate the clients to ensure that each of them can benefit from others. To address these concerns, we present a novel decentralized framework, FedeRiCo, where each client can learn as much or as little from other clients as is optimal for its local data distribution. Based on expectation-maximization, FedeRiCo estimates the utilities of other participants' models on each client's data so that everyone can select the right collaborators for learning. As a result, our algorithm outperforms other federated, personalized, and/or decentralized approaches on several benchmark datasets, being the only approach that consistently performs better than training with local data only.
ISSN:2331-8422