FedPBS通过动态调整批次大小提升联邦学习在非独立同分布数据下的稳定性与性能。
FedPBS: Proximal-Balanced Scaling Federated Learning Model for Robust Personalized Training for Non-IID Data
- 动态调整客户端批次大小，平衡资源差异
- 对小批量客户端引入近端修正，减少模型偏差
- 适合医疗、金融等非独立同分布数据场景
联邦学习（FL）使分布式客户端在保护本地数据隐私的前提下联合训练模型，适用于医疗、金融、智能城市等领域。然而，统计异质性和客户端参与不均会降低收敛速度与模型质量。本文提出FedPBS，结合FedBS与FedProx的互补思想：动态根据客户端资源调整批大小以实现均衡参与；对小批量客户端选择性应用近端修正，稳定局部更新并减少与全局模型的偏离。在CIFAR-10和UCI-HAR等基准数据集上，于高度非独立同分布条件下，FedPBS持续优于当前最优方法（如FedBS、FedGA、MOON、FedProx），在极端数据异质性下表现稳健，损失曲线平滑，表明在多样化联邦环境中具有稳定可靠的收敛性。
原文摘要 · Abstract (English)
Federated learning (FL) enables a set of distributed clients to jointly train machine learning models while preserving their local data privacy, making it attractive for applications in healthcare, finance, mobility, and smart-city systems. However, FL faces several challenges, including statistical heterogeneity and uneven client participation, which can degrade convergence and model quality. In this work, we propose FedPBS, an FL algorithm that couples complementary ideas from FedBS and FedProx to address these challenges. FedPBS dynamically adapts batch sizes to client resources to support balanced and scalable participation, and selectively applies a proximal correction to small-batch clients to stabilize local updates and reduce divergence from the global model. Experiments on benchmarking datasets such as CIFAR-10 and UCI-HAR under highly non-IID settings demonstrate that FedPBS consistently outperforms state-of-the-art methods, including FedBS, FedGA, MOON, and FedProx. The results demonstrate robust performance gains under extreme data heterogeneity, with smooth loss curves indicating stable convergence across diverse federated environments. FedPBS consistently outperforms state-of-the-art federated learning baselines on UCI-HAR and CIFAR-10 under severe non-IID conditions while maintaining stable and reliable convergence.
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