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Find Your Friends: Personalized Federated Learning with the Right Collaborators

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arxiv 2210.06597 v2 pith:XVMQFACJ submitted 2022-10-12 cs.LG

Yi Sui , Junfeng Wen , Yenson Lau , Brendan Leigh Ross , Jesse C. Cresswell This is my paper · ORCID
classification cs.LG
keywords clientsdatafederatedlearningothercentralclientcollaborators
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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. pFedWN: A Personalized Federated Learning Framework for D2D Wireless Networks with Heterogeneous Data

    cs.LG 2025-01 reject novelty 5.0 of 10

    pFedWN combines channel-aware neighbor selection with an EM-based model weighting step to personalize federated learning over server-free D2D wireless networks.

  2. S-VOTE: Similarity-based Voting for Client Selection in Decentralized Federated Learning

    cs.LG 2025-01 conditional novelty 4.0 of 10

    S-VOTE selects clients by cosine similarity of model weights and lets low-vote clients sometimes skip training, reducing communication and energy while improving non-IID accuracy in some settings.

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