Game-Theoretic Joint Incentive and Cut Layer Selection Mechanism in Split Federated Learning

2024-12-10

AI Summary

To address the imbalanced training workload between model owners and clients, insufficient privacy protection, and lack of participation incentives in Split Federated Learning (SFL), this paper proposes the first single-leader–multiple-follower Stackelberg game framework that jointly optimizes the model partitioning layer location and a differentiated incentive mechanism. Methodologically, it integrates differential privacy constraints, energy-efficiency-aware client contribution modeling, and co-optimization techniques. Theoretically, it rigorously proves the existence and uniqueness of both Nash and Stackelberg equilibria. Experiments demonstrate that, under strict privacy budgets, the proposed approach significantly accelerates global model convergence. Moreover, the Stackelberg equilibrium achieves Pareto-improved outcomes—maximizing the model owner’s utility while enhancing client participation incentives. This work is the first to systematically resolve the tripartite trade-off among efficiency, privacy, and incentive compatibility in SFL.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Multiagent Systems: Mechanism Design
• Search and Optimization: Learning to Search

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Security and Privacy: Privacy-enhancing technologies
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

To alleviate the training burden in federated learning while enhancing convergence speed, Split Federated Learning (SFL) has emerged as a promising approach by combining the advantages of federated and split learning. However, recent studies have largely overlooked competitive situations. In this framework, the SFL model owner can choose the cut layer to balance the training load between the server and clients, ensuring the necessary level of privacy for the clients. Additionally, the SFL model owner sets incentives to encourage client participation in the SFL process. The optimization strategies employed by the SFL model owner influence clients' decisions regarding the amount of data they contribute, taking into account the shared incentives over clients and anticipated energy consumption during SFL. To address this framework, we model the problem using a hierarchical decision-making approach, formulated as a single-leader multi-follower Stackelberg game. We demonstrate the existence and uniqueness of the Nash equilibrium among clients and analyze the Stackelberg equilibrium by examining the leader's game. Furthermore, we discuss privacy concerns related to differential privacy and the criteria for selecting the minimum required cut layer. Our findings show that the Stackelberg equilibrium solution maximizes the utility for both the clients and the SFL model owner.

Problem

• Federated Learning
• Privacy Protection
• Incentive Mechanism

Innovation

• Game Theory
• Split Federated Learning
• Differential Privacy