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Personalized federated learning considers learning models unique to eachclient in a heterogeneous network. The resulting client-specific models havebeen purported to improve metrics such as accuracy, fairness, and robustness infederated networks. However, despite a plethora of work in this area, itremains unclear: (1) which personalization techniques are most effective invarious settings, and (2) how important personalization truly is for realisticfederated applications. To better answer these questions, we propose Motley, abenchmark for personalized federated learning. Motley consists of a suite ofcross-device and cross-silo federated datasets from varied problem domains, aswell as thorough evaluation metrics for better understanding the possibleimpacts of personalization. We establish baselines on the benchmark bycomparing a number of representative personalized federated learning methods.These initial results highlight strengths and weaknesses of existingapproaches, and raise several open questions for the community. Motley aims toprovide a reproducible means with which to advance developments in personalizedand heterogeneity-aware federated learning, as well as the related areas oftransfer learning, meta-learning, and multi-task learning.

Motley: Benchmarking Heterogeneity and Personalization in Federated Learning