  Since data is presented long-tailed in reality, it is challenging for
Federated Learning (FL) to train across decentralized clients as practical
applications. We present Global-Regularized Personalization (GRP-FED) to tackle
the data imbalanced issue by considering a single global model and multiple
local models for each client. With adaptive aggregation, the global model
treats multiple clients fairly and mitigates the global long-tailed issue. Each
local model is learned from the local data and aligns with its distribution for
customization. To prevent the local model from just overfitting, GRP-FED
applies an adversarial discriminator to regularize between the learned
global-local features. Extensive results show that our GRP-FED improves under
both global and local scenarios on real-world MIT-BIH and synthesis CIFAR-10
datasets, achieving comparable performance and addressing client imbalance.
