This study presents Weighted Sampled Split Learning (WSSL), an innovative framework tailored to bolster privacy, robustness, and fairness in distributed machine learning systems. Unlike traditional approaches, WSSL disperses the learning process among multiple clients, thereby safeguarding data confidentiality. Central to WSSL's efficacy is its utilization of weighted sampling. This approach ensures equitable learning by tactically selecting influential clients based on their contributions. Our evaluation of WSSL spanned various client configurations and employed two distinct datasets: Human Gait Sensor and CIFAR-10. We observed three primary benefits: heightened model accuracy, enhanced robustness, and maintained fairness across diverse client compositions. Notably, our distributed frameworks consistently surpassed centralized counterparts, registering accuracy peaks of 82.63% and 75.51% for the Human Gait Sensor and CIFAR-10 datasets, respectively. These figures contrast with the top accuracies of 81.12% and 58.60% achieved by centralized systems. Collectively, our findings champion WSSL as a potent and scalable successor to conventional centralized learning, marking it as a pivotal stride forward in privacy-focused, resilient, and impartial distributed machine learning.

翻译：本研究提出加权采样分割学习（WSSL），这是一种专为增强分布式机器学习系统中隐私、鲁棒性和公平性而设计的创新框架。与传统方法不同，WSSL将学习过程分散到多个客户端，从而保障数据机密性。其核心效能在利用加权采样机制，通过基于贡献度策略性地选择具有影响力的客户端实现公平学习。我们在多种客户端配置下对WSSL进行了评估，并采用了两个不同数据集：Human Gait Sensor和CIFAR-10。我们观察到三项主要优势：更高的模型精度、增强的鲁棒性以及在多样化客户端构成中维持的公平性。值得注意的是，我们的分布式框架在Human Gait Sensor和CIFAR-10数据集上分别取得了82.63%和75.51%的峰值精度，持续超越集中式系统——后者最优精度仅为81.12%和58.60%。综合而言，本研究的发现证实WSSL是传统集中式学习的有力且可扩展的替代方案，标志着在注重隐私、鲁棒且公正的分布式机器学习领域迈出了关键一步。