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Most existing federated learning algorithms are based on the vanilla FedAvgscheme. However, with the increase of data complexity and the number of modelparameters, the amount of communication traffic and the number of iterationrounds for training such algorithms increases significantly, especially innon-independently and homogeneously distributed scenarios, where they do notachieve satisfactory performance. In this work, we propose FedND: federatedlearning with noise distillation. The main idea is to use knowledgedistillation to optimize the model training process. In the client, we proposea self-distillation method to train the local model. In the server, we generatenoisy samples for each client and use them to distill other clients. Finally,the global model is obtained by the aggregation of local models. Experimentalresults show that the algorithm achieves the best performance and is morecommunication-efficient than state-of-the-art methods.

Federated Learning on Non-iid Data via Local and Global Distillation