  Federated Learning (FL) enables multiple clients to collaboratively learn in
a distributed way, allowing for privacy protection. However, the real-world
non-IID data will lead to client drift which degrades the performance of FL.
Interestingly, we find that the difference in logits between the local and
global models increases as the model is continuously updated, thus seriously
deteriorating FL performance. This is mainly due to catastrophic forgetting
caused by data heterogeneity between clients. To alleviate this problem, we
propose a new algorithm, named FedCSD, a Class prototype Similarity
Distillation in a federated framework to align the local and global models.
FedCSD does not simply transfer global knowledge to local clients, as an
undertrained global model cannot provide reliable knowledge, i.e., class
similarity information, and its wrong soft labels will mislead the optimization
of local models. Concretely, FedCSD introduces a class prototype similarity
distillation to align the local logits with the refined global logits that are
weighted by the similarity between local logits and the global prototype. To
enhance the quality of global logits, FedCSD adopts an adaptive mask to filter
out the terrible soft labels of the global models, thereby preventing them to
mislead local optimization. Extensive experiments demonstrate the superiority
of our method over the state-of-the-art federated learning approaches in
various heterogeneous settings. The source code will be released.
