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In the traditional federated learning setting, a central server coordinates anetwork of clients to train one global model. However, the global model mayserve many clients poorly due to data heterogeneity. Moreover, there may notexist a trusted central party that can coordinate the clients to ensure thateach of them can benefit from others. To address these concerns, we present anovel decentralized framework, FedeRiCo, where each client can learn as much oras little from other clients as is optimal for its local data distribution.Based on expectation-maximization, FedeRiCo estimates the utilities of otherparticipants' models on each client's data so that everyone can select theright collaborators for learning. As a result, our algorithm outperforms otherfederated, personalized, and/or decentralized approaches on several benchmarkdatasets, being the only approach that consistently performs better thantraining with local data only.

Find Your Friends: Personalized Federated Learning with the Right Collaborators