A Service-Oriented Adaptive Hierarchical Incentive Mechanism for Federated Learning

2025/09/03 by Jiaxing Cao, Cao, Jiaxing, Yuzhou Gao +3
Computer Science · #Blockchain Technology Applications and Security #Computer Science and Game Theory (cs.GT) #Cryptography and Data Security #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Privacy-Preserving Technologies in Data #Systems and Control (eess.SY) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2509.10512

openalex publication_date 2025/09/03 · openalex created_date 2025/10/12 · openalex updated_date 2026/07/28

Abstract

Recently, federated learning (FL) has emerged as a novel framework for distributed model training. In FL, the task publisher (TP) releases tasks, and local model owners (LMOs) use their local data to train models. Sometimes, FL suffers from the lack of training data, and thus workers are recruited for gathering data. To this end, this paper proposes an adaptive incentive mechanism from a service-oriented perspective, with the objective of maximizing the utilities of TP, LMOs and workers. Specifically, a Stackelberg game is theoretically established between the LMOs and TP, positioning TP as the leader and the LMOs as followers. An analytical Nash equilibrium solution is derived to maximize their utilities. The interaction between LMOs and workers is formulated by a multi-agent Markov decision process (MAMDP), with the optimal strategy identified via deep reinforcement learning (DRL). Additionally, an Adaptively Searching the Optimal Strategy Algorithm (ASOSA) is designed to stabilize the strategies of each participant and solve the coupling problems. Extensive numerical experiments are conducted to validate the efficacy of the proposed method.

Citations

• Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions
• A Systematic Literature Review on Federated Learning: From A Model Quality Perspective
• Dynamic Fusion based Federated Learning for COVID-19 Detection
• Federated Learning in Mobile Edge Networks: A Comprehensive Survey
• Federated Learning in Mobile Edge Networks: A Comprehensive Survey
• Matrix Sketching for Secure Collaborative Machine Learning
• Federated Optimization in Heterogeneous Networks
• Federated Learning for Mobile Keyboard Prediction
• Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
• Proximal Policy Optimization Algorithms

Related

• Nonlinear Equilibrium Transitions in a Potential Game Model for Federated Learning
• Incentive Mechanisms for Federated Learning: From Economic and Game Theoretic Perspective
• Adaptive incentive for cross-silo federated learning: A multi-agent reinforcement learning approach
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