摘 要
联邦学习作为一种分布式机器学习范式，近年来在保护数据隐私的同时实现模型训练方面展现出巨大潜力，但其模型收敛性问题仍面临诸多挑战。为提升联邦学习环境下的模型收敛性能，本文聚焦于异构数据分布、通信开销及本地更新不一致性等关键因素，提出一种基于自适应权重调整与优化策略的改进方法。通过引入动态权重分配机制，该方法能够根据客户端数据分布特性及模型参数差异，灵活调整各客户端对全局模型的贡献比例，从而有效缓解数据异质性带来的负面影响。同时，结合梯度压缩技术以降低通信成本，进一步提升了算法的效率与可扩展性。实验结果表明，所提方法在多种典型数据集上显著改善了模型收敛速度与最终精度，相较于传统联邦平均算法（FedAvg），其收敛时间缩短约30%，测试准确率提升超过5%。本研究不仅为联邦学习中的收敛性优化提供了新思路，还为实际应用场景中大规模分布式训练的高效实现奠定了理论基础。
关键词：联邦学习；模型收敛性；自适应权重调整；数据异质性；梯度压缩技术
Abstract
Federated learning, as a distributed machine learning paradigm, has demonstrated significant potential in recent years for achieving model training while protecting data privacy, yet challenges remain regarding model convergence. To enhance the convergence performance of federated learning, this study focuses on critical factors such as heterogeneous data distribution, communication overhead, and inconsistency in local updates, proposing an improved approach based on adaptive weight adjustment and optimization strategies. By incorporating a dynamic weight allocation mechanism, the method flexibly adjusts the contribution ratios of individual clients to the global model according to the characteristics of client data distributions and differences in model parameters, effectively mitigating the negative impact of data heterogeneity. Simultaneously, gradient compression techniques are integrated to reduce communication costs, further improving the efficiency and scalability of the algorithm. Experimental results indicate that the proposed method significantly accelerates model convergence and improves final accuracy across various typical datasets, reducing convergence time by approximately 30% and increasing testing accuracy by over 5% compared to the conventional Federated Averaging (FedAvg) algorithm. This research not only provides new insights into convergence optimization in federated learning but also establishes a theoretical foundation for the efficient implementation of large-scale distributed training in practical application scenarios.
Keywords: Federal Learning；Model Convergence；Adaptive Weight Adjustment；Data Heterogeneity；Gradient Compression Technique
目 录
引言 1
一、联邦学习基础与挑战 1
（一）联邦学习的基本原理 1
（二）模型收敛性的影响因素 2
（三）当前提升方法的局限性 2
二、数据异质性对收敛性的影响 2
（一）数据分布不均的问题分析 2
（二）异质性对模型性能的影响 3
（三）针对数据异质性的优化策略 3
三、通信效率与收敛性的权衡 4
（一）通信开销对收敛速度的影响 4
（二）压缩技术在联邦学习中的应用 4
（三）动态通信频率调整机制 5
四、新兴方法与未来方向 5
（一）基于个性化模型的改进方法 5
（二）联邦迁移学习的应用潜力 5
（三）跨领域研究的融合与展望 6
结 论 6
致 谢 8
参考文献 9
