Distribution-Aware Compensation Design for Sustainable Data Rights in Machine Learning

• URL: http://arxiv.org/abs/2410.15045v2
• Date: Thu, 24 Oct 2024 01:25:51 GMT
• Title: Distribution-Aware Compensation Design for Sustainable Data Rights in Machine Learning
• Authors: Jiaqi Shao, Tao Lin, Bing Luo,
• Abstract summary: We propose an innovative mechanism that views this challenge through the lens of game theory. Our approach quantifies the ripple effects of data removal through a comprehensive analytical model. We establish mathematical foundations for measuring participant utility and system outcomes.
• Score: 6.322978909154803
• License:
• Abstract: Modern distributed learning systems face a critical challenge when clients request the removal of their data influence from trained models, as this process can significantly destabilize system performance and affect remaining participants. We propose an innovative mechanism that views this challenge through the lens of game theory, establishing a leader-follower framework where a central coordinator provides strategic incentives to maintain system stability during data removal operations. Our approach quantifies the ripple effects of data removal through a comprehensive analytical model that captures both system-wide and participant-specific impacts. We establish mathematical foundations for measuring participant utility and system outcomes, revealing critical insights into how data diversity influences both individual decisions and overall system stability. The framework incorporates a computationally efficient solution method that addresses the inherent complexity of optimizing participant interactions and resource allocation.

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