FedL2P: Federated Learning to Personalize
Royson Lee Minyoung Kim Da Li Xinchi Qiu Timothy Hospedales Ferenc Huszár Nicholas D. Lane
Federated learning (FL) research has made progress in developing algorithms
for distributed learning of global models, as well as algorithms for local
personalization of those common models to the specifics of each client's local
data distribution. However, different FL problems may require different
personalization strategies, and it may not even be possible to define an
effective one-size-fits-all personalization strategy for all clients: depending
on how similar each client's optimal predictor is to that of the global model,
different personalization strategies may be preferred. In this paper, we
consider the federated meta-learning problem of learning personalization
strategies. Specifically, we consider meta-nets that induce the batch-norm and
learning rate parameters for each client given local data statistics. By
learning these meta-nets through FL, we allow the whole FL network to
collaborate in learning a customized personalization strategy for each client.
Empirical results show that this framework improves on a range of standard
hand-crafted personalization baselines in both label and feature shift
situations.