  Data heterogeneity across clients is one of the key challenges in Federated
Learning (FL), which may slow down the global model convergence and even weaken
global model performance. Most existing approaches tackle the heterogeneity by
constraining local model updates through reference to global information
provided by the server. This can alleviate the performance degradation on the
aggregated global model. Different from existing methods, we focus the
information exchange between clients, which could also enhance the
effectiveness of local training and lead to generate a high-performance global
model. Concretely, we propose a novel FL framework named FedCME by client
matching and classifier exchanging. In FedCME, clients with large differences
in data distribution will be matched in pairs, and then the corresponding pair
of clients will exchange their classifiers at the stage of local training in an
intermediate moment. Since the local data determines the local model training
direction, our method can correct update direction of classifiers and
effectively alleviate local update divergence. Besides, we propose feature
alignment to enhance the training of the feature extractor. Experimental
results demonstrate that FedCME performs better than FedAvg, FedProx, MOON and
FedRS on popular federated learning benchmarks including FMNIST and CIFAR10, in
the case where data are heterogeneous.
