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We present PeFLL, a new personalized federated learning algorithm thatimproves over the state-of-the-art in three aspects: 1) it produces moreaccurate models, especially in the low-data regime, and not only for clientspresent during its training phase, but also for any that may emerge in thefuture; 2) it reduces the amount of on-client computation and client-servercommunication by providing future clients with ready-to-use personalized modelsthat require no additional finetuning or optimization; 3) it comes withtheoretical guarantees that establish generalization from the observed clientsto future ones. At the core of PeFLL lies a learning-to-learn approach thatjointly trains an embedding network and a hypernetwork. The embedding networkis used to represent clients in a latent descriptor space in a way thatreflects their similarity to each other. The hypernetwork takes as input suchdescriptors and outputs the parameters of fully personalized client models. Incombination, both networks constitute a learning algorithm that achievesstate-of-the-art performance in several personalized federated learningbenchmarks.

PeFLL: Personalized Federated Learning by Learning to Learn