  Federated learning (FL) is an emerging technique used to collaboratively
train a global machine learning model while keeping the data localized on the
user devices. The main obstacle to FL's practical implementation is the
Non-Independent and Identical (Non-IID) data distribution across users, which
slows convergence and degrades performance. To tackle this fundamental issue,
we propose a method (ComFed) that enhances the whole training process on both
the client and server sides. The key idea of ComFed is to simultaneously
utilize client-variance reduction techniques to facilitate server aggregation
and global adaptive update techniques to accelerate learning. Our experiments
on the Cifar-10 classification task show that ComFed can improve
state-of-the-art algorithms dedicated to Non-IID data.
