  Personalized federated learning (PFL) jointly trains a variety of local
models through balancing between knowledge sharing across clients and model
personalization per client. This paper addresses PFL via explicit disentangling
latent representations into two parts to capture the shared knowledge and
client-specific personalization, which leads to more reliable and effective
PFL. The disentanglement is achieved by a novel Federated Dual Variational
Autoencoder (FedDVA), which employs two encoders to infer the two types of
representations. FedDVA can produce a better understanding of the trade-off
between global knowledge sharing and local personalization in PFL. Moreover, it
can be integrated with existing FL methods and turn them into personalized
models for heterogeneous downstream tasks. Extensive experiments validate the
advantages caused by disentanglement and show that models trained with
disentangled representations substantially outperform those vanilla methods.
