Find Your Friends: Personalized Federated Learning with the Right Collaborators
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.
Keywords
Cite
@article{arxiv.2210.06597,
title = {Find Your Friends: Personalized Federated Learning with the Right Collaborators},
author = {Yi Sui and Junfeng Wen and Yenson Lau and Brendan Leigh Ross and Jesse C. Cresswell},
journal= {arXiv preprint arXiv:2210.06597},
year = {2022}
}