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Find Your Friends: Personalized Federated Learning with the Right Collaborators
Yi Sui · Junfeng Wen · Yenson Lau · Brendan Ross · Jesse Cresswell

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in Federated Learning: Recent Advances and New Challenges »
Event URL: https://openreview.net/forum?id=9916eknWHOr »

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.

Author Information

Yi Sui (Layer 6 AI)
Junfeng Wen (University of Alberta)
Yenson Lau (Columbia University)
Brendan Ross (Layer 6 AI)
Jesse Cresswell (Layer 6 AI)

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