  Most existing federated learning algorithms are based on the vanilla FedAvg
scheme. However, with the increase of data complexity and the number of model
parameters, the amount of communication traffic and the number of iteration
rounds for training such algorithms increases significantly, especially in
non-independently and homogeneously distributed scenarios, where they do not
achieve satisfactory performance. In this work, we propose FedND: federated
learning with noise distillation. The main idea is to use knowledge
distillation to optimize the model training process. In the client, we propose
a self-distillation method to train the local model. In the server, we generate
noisy samples for each client and use them to distill other clients. Finally,
the global model is obtained by the aggregation of local models. Experimental
results show that the algorithm achieves the best performance and is more
communication-efficient than state-of-the-art methods.
