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This study presents Weighted Sampled Split Learning (WSSL), an innovativeframework tailored to bolster privacy, robustness, and fairness in distributedmachine learning systems. Unlike traditional approaches, WSSL disperses thelearning process among multiple clients, thereby safeguarding dataconfidentiality. Central to WSSL's efficacy is its utilization of weightedsampling. This approach ensures equitable learning by tactically selectinginfluential clients based on their contributions. Our evaluation of WSSLspanned various client configurations and employed two distinct datasets: HumanGait Sensor and CIFAR-10. We observed three primary benefits: heightened modelaccuracy, enhanced robustness, and maintained fairness across diverse clientcompositions. Notably, our distributed frameworks consistently surpassedcentralized counterparts, registering accuracy peaks of 82.63% and 75.51% forthe Human Gait Sensor and CIFAR-10 datasets, respectively. These figurescontrast with the top accuracies of 81.12% and 58.60% achieved by centralizedsystems. Collectively, our findings champion WSSL as a potent and scalablesuccessor to conventional centralized learning, marking it as a pivotal strideforward in privacy-focused, resilient, and impartial distributed machinelearning.

Weighted Sampled Split Learning (WSSL): Balancing Privacy, Robustness, and Fairness in Distributed Learning Environments