摘要： 本文针对联邦学习在医学影像分类任务中面临的系统异构性与数据异构性双重挑战，提出一种基于强化学习的自适应联邦优化算法SEFedProX。该算法在异构环境下采用Soft Actor-Critic算法，基于客户端数据分布、性能反馈等关键状态特征，在连续动作空间中动态调整近端项系数，有效克服离散动作空间引发的量化误差与模型振荡问题，实现了对本地训练强度的精准平滑控制。同时，引入在ImageNet上预训练的EfficientNetV2B2作为特征提取网络，在提升模型表征效率与判别能力的同时，显著降低对资源受限医疗边缘设备的部署要求，缓解了小规模医学数据下的过拟合风险。在四种不同系统异构性设置下，基于四个医学影像数据集和一个通用数据集的系统性实验结果表明，SEFedProX在分类精度、收敛速度、稳定性与鲁棒性方面均显著优于现有基线方法。消融实验进一步验证了SAC连续调控机制与EfficientNetV2B2网络各自的有效性及其在算法中的协同增强作用。本研究为异构医疗环境下分布式智能诊断系统的构建提供了一种稳定、高效且具备强自适应能力的技术方案。

Abstract: This paper addresses the dual challenges of system heterogeneity and data heterogeneity faced by federated learning in the medical image classification task, and proposes an adaptive federated optimization framework named SEFedProX based on reinforcement learning. This framework employs the Soft Actor-Critic algorithm in an heterogeneous environment, based on key state features such as client data distribution and performance feedback, and dynamically adjusts the proximal term coefficients in the continuous action space, effectively overcoming the quantization errors and model oscillation problems caused by discrete action spaces, and achieving precise and smooth control of the local training intensity. At the same time, an EfficientNetV2B2 pre-trained on ImageNet is introduced as the feature extraction network, which improves the model's representation efficiency and discrimination ability while significantly reducing the deployment requirements for resource-constrained medical edge devices, alleviating the overfitting risk in small-scale medical data. Systematic experimental results based on four different system heterogeneity settings and four medical image datasets and a general dataset show that SEFedProX significantly outperforms existing baseline methods in terms of classification accuracy, convergence speed, stability, and robustness. Ablation experiments further verify the effectiveness of the SAC continuous regulation mechanism and the EfficientNetV2B2 network, as well as their collaborative enhancement effect in the framework. This research provides a stable, efficient, and highly adaptive technical solution for the construction of distributed intelligent diagnostic systems in heterogeneous medical environments.