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A Service-Oriented Adaptive Hierarchical Incentive Mechanism for Federated Learning

arXiv 2509.10512 ↗First posted 3 Sep 2025cs.LG cs.GT cs.SYcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 31 references that are in the map. First posted on arXiv 3 Sep 2025.

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10 of 31 references.

References · 10

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  3. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,349
  4. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  5. 2018Federated Learning for Mobile Keyboard Predictioncited by 572
  6. 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 408
  7. 2020Dynamic Fusion based Federated Learning for COVID-19 Detectioncited by 36
  8. 2022Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directionscited by 35
  9. 2020A Systematic Literature Review on Federated Learning: From A Model Quality Perspectivecited by 15
  10. 2019Matrix Sketching for Secure Collaborative Machine Learningcited by 7

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  5. 2024An Auction-based Marketplace for Model Trading in Federated Learning
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  7. 2025A Survey of Multi Agent Reinforcement Learning: Federated Learning and Cooperative and Noncooperative Decentralized Regimes
  8. 2024FRESCO: Federated Reinforcement Energy System for Cooperative Optimization