Federated Learning (FL) facilitates collaborative machine learning by training models on local datasets, and subsequently aggregating these local models at a central server. However, the frequent exchange of model parameters between clients and the central server can result in significant communication overhead during the FL training process. To solve this problem, this paper proposes a novel FL framework, the Model Aggregation with Layer Divergence Feedback mechanism (FedLDF). Specifically, we calculate model divergence between the local model and the global model from the previous round. Then through model layer divergence feedback, the distinct layers of each client are uploaded and the amount of data transferred is reduced effectively. Moreover, the convergence bound reveals that the access ratio of clients has a positive correlation with model performance. Simulation results show that our algorithm uploads local models with reduced communication overhead while upholding a superior global model performance.

翻译：联邦学习（FL）通过在本地数据集上训练模型，并在中央服务器上聚合这些本地模型，从而实现协同机器学习。然而，在FL训练过程中，客户端与中央服务器之间频繁的模型参数交换会导致显著的通信开销。针对该问题，本文提出了一种新颖的FL框架——基于层级差异反馈机制的模型聚合（FedLDF）。具体而言，我们计算上一轮本地模型与全局模型之间的模型差异。随后，通过模型层级差异反馈，每个客户端的不同层级被上传，从而有效减少传输的数据量。此外，收敛界表明客户端的访问比例与模型性能呈正相关。仿真结果表明，我们的算法在保持优越的全局模型性能的同时，以更低的通信开销上传本地模型。