  Federated learning enables multiple clients to collaboratively learn a global
model by periodically aggregating the clients' models without transferring the
local data. However, due to the heterogeneity of the system and data, many
approaches suffer from the "client-drift" issue that could significantly slow
down the convergence of the global model training. As clients perform local
updates on heterogeneous data through heterogeneous systems, their local models
drift apart. To tackle this issue, one intuitive idea is to guide the local
model training by the global teachers, i.e., past global models, where each
client learns the global knowledge from past global models via adaptive
knowledge distillation techniques. Coming from these insights, we propose a
novel approach for heterogeneous federated learning, namely FedGKD, which fuses
the knowledge from historical global models for local training to alleviate the
"client-drift" issue. In this paper, we evaluate FedGKD with extensive
experiments on various CV/NLP datasets (i.e., CIFAR-10/100, Tiny-ImageNet, AG
News, SST5) and different heterogeneous settings. The proposed method is
guaranteed to converge under common assumptions, and achieves superior
empirical accuracy in fewer communication runs than five state-of-the-art
methods.
