  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.
