Personalized federated learning (pFL) has emerged as a popular approach to
dealing with the challenge of statistical heterogeneity between the data
distributions of the participating clients. Instead of learning a single global
model, pFL aims to learn an individual model for each client while still making
use of the data available at other clients. In this work, we present PeFLL, a
new pFL approach rooted in lifelong learning that performs well not only on
clients present during its training phase, but also on any that may emerge in
the future. PeFLL learns to output client specific models by jointly training
an embedding network and a hypernetwork. The embedding network learns to
represent clients in a latent descriptor space in a way that reflects their
similarity to each other. The hypernetwork learns a mapping from this latent
space to the space of possible client models. We demonstrate experimentally
that PeFLL produces models of superior accuracy compared to previous methods,
especially for clients not seen during training, and that it scales well to
large numbers of clients. Moreover, generating a personalized model for a new
client is efficient as no additional fine-tuning or optimization is required by
either the client or the server. We also present theoretical results supporting
PeFLL in the form of a new PAC-Bayesian generalization bound for lifelong
learning and we prove the convergence of our proposed optimization procedure.

本文提出了一种名为 PeFLL 的个性化联邦学习方法，通过联合训练嵌入网络和超级网络来输出特定客户端的模型，并证明了其较以往方法在精度和规模等方面都更具优势，还提供了一种新的理论结果以支持该方法的可行性。