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.

本文提出一种名为FedND的新型联邦学习算法，采用知识蒸馏优化模型训练过程，并在客户端使用自我蒸馏方法进行本地模型训练，在服务器端生成噪声样本用于蒸馏其他客户端，最终通过聚合本地模型获得全局模型，实验结果表明该算法不仅达到最佳性能，而且比现有的算法更具通信效率。

通过本地和全局的蒸馏在非独立同分布数据上进行联邦学习