Federated learning is widely used to learn intelligent models from decentralized data. In federated learning, clients need to communicate their local model updates in each iteration of model learning. However, model updates are large in size if the model contains numerous parameters, and there usually needs many rounds of communication until model converges. Thus, the communication cost in federated learning can be quite heavy. In this paper, we propose a communication efficient federated learning method based on knowledge distillation. Instead of directly communicating the large models between clients and server, we propose an adaptive mutual distillation framework to reciprocally learn a student and a teacher model on each client, where only the student model is shared by different clients and updated collaboratively to reduce the communication cost. Both the teacher and student on each client are learned on its local data and the knowledge distilled from each other, where their distillation intensities are controlled by their prediction quality. To further reduce the communication cost, we propose a dynamic gradient approximation method based on singular value decomposition to approximate the exchanged gradients with dynamic precision. Extensive experiments on benchmark datasets in different tasks show that our approach can effectively reduce the communication cost and achieve competitive results.

本研究提出了一种基于知识蒸馏的通信高效的联邦学习方法，通过在客户端上互相学习一个学生模型和一个教师模型，只共享学生模型以降低通信成本，并提出了一种基于奇异值分解的动态梯度逼近方法来进一步降低通信成本。实验表明，该方法能够有效减少通信成本并取得竞争性的结果。

基于知识蒸馏的通信高效联邦学习(FedKD)