关键词: 联邦学习, 去中心化, 真实网络环境, 时延敏感, 通信成本

Abstract: In recent years,the rapid development of deep learning,mobile devices,and IoT technology has led to a surge in demand for model inference and data storage on edge devices.Traditional centralized model training methods are limited by datavo-lume,communication bandwidth,and user data privacy issues and cannot effectively address the new challenges.Therefore,federated learning technology is born.Federated learning allows edge devices to train models based on local data and upload model parameters to a central server for aggregation and distribution,ensuring that joint modeling can be performed without data leaving the trusted domain of each party.Furthermore,distributed federated learning has been developed to overcome issues such as latency,bandwidth limitations,and single point of failure risks.However,the training efficiency of federated learning is severely affected by real-world network delay and bandwidth factors,making multi-party joint modeling difficult.To address this issue,this paper proposes a decentralized federated learning algorithm DBFedAvg that dynamically selects nodes with lower average delay as the main nodes to reduce communication costs and improve global model training performance,accelerating model convergence.Experimental results on the Sprint network and other scenarios have validated that the proposed method brings significant improvements in communication costs and model convergence.

Key words: Federated learning, Decentralized, Real network environment, Delay-based, Communication cost