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, but may serve many clients poorly due to data heterogeneity. We present a 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.