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.

翻译：联邦学习（FL）使一组分布式客户端能够在不泄露本地数据隐私的前提下联合训练机器学习模型，因此其在医疗、金融、移动及智慧城市系统等领域具有显著吸引力。然而，FL面临统计异质性与客户端参与不均衡等挑战，这可能导致收敛性能下降与模型质量退化。本文提出FedPBS算法，该算法融合了FedBS与FedProx的互补思想，以应对上述挑战。FedPBS根据客户端资源动态调整批次大小，实现均衡可扩展的参与机制，并对小批次客户端选择性施加近端矫正，从而稳定局部更新并减少与全局模型的偏差。在CIFAR-10与UCI-HAR等基准数据集上的强非独立同分布场景实验表明，FedPBS持续优于FedBS、FedGA、MOON及FedProx等前沿方法。结果证明，在极端数据异质条件下，FedPBS展现出稳健的性能增益，其平滑的损失曲线表明算法在不同联邦环境中均能稳定收敛。在严重非独立同分布条件下，FedPBS在UCI-HAR与CIFAR-10数据集上持续超越各联邦学习基线方法，同时保持稳定可靠的收敛特性。