Find Your Friends: Personalized Federated Learning with the Right Collaborators

Yi Sui, Junfeng Wen, Yenson Lau, Brendan Leigh Ross, Jesse C Cresswell

Keywords: Federated learning, Personalized federated learning, Decentralized federated learning

TL;DR: We propose a novel personalized decentralized federated learning framework for heterogeneous client data by collaborating with the right clients.

Abstract: 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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Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/find-your-friends-personalized-federated/code)