Daily Arxiv

This is a page that curates AI-related papers published worldwide.
All content here is summarized using Google Gemini and operated on a non-profit basis.
Copyright for each paper belongs to the authors and their institutions; please make sure to credit the source when sharing.

Pareto Actor-Critic for Communication and Computation Co-Optimization in Non-Cooperative Federated Learning Services

Created by
  • Haebom

Author

Renxuan Tan, Rongpeng Li, Xiaoxue Yu, Xianfu Chen, Xing Xu, Zhifeng Zhao

Outline

This paper presents PAC-MCoFL, a game-theoretic multi-agent reinforcement learning (MARL) framework for addressing the non-cooperative dynamics of federated learning (FL) in a multi-service provider (SP) ecosystem. PAC-MCoFL treats service providers as agents and jointly optimizes client assignment, adaptive quantization, and resource allocation. It integrates the Pareto Actor-Critic (PAC) principle and predictive regression to achieve Pareto-optimal equilibrium, models heterogeneous risk profiles, and efficiently manages high-dimensional action spaces through a trinomial Cartesian decomposition (TCAD) mechanism. Furthermore, we develop a scalable variant, PAC-MCoFL-p, featuring a parameterized guess generator that significantly reduces computational complexity and tightly bounds the error. Extensive simulations, along with theoretical convergence guarantees, demonstrate its superiority over existing state-of-the-art MARL solutions, improving total reward and hypervolume index (HVI) by approximately 5.8% and 4.2%, respectively.

Takeaways, Limitations

•
Takeaways:
◦
A novel game-theoretic MARL framework is presented to enhance the efficiency of federated learning in multi-service provider environments.
◦
Achieving Pareto optimal equilibrium and modeling heterogeneous risk profiles using the PAC principle and predictive regression.
◦
Efficient management of high-dimensional action spaces through TCAD mechanisms.
◦
Reduced computational complexity and bounded error through scalable variants of PAC-MCoFL-p.
◦
Demonstrating superiority over existing methods through total compensation and HVI enhancement.
◦
Achieving effective balance between individual SP and system performance in diverse data heterogeneity and scaled deployment environments.
•
Limitations:
◦
Lack of experimental validation in real multi-service provider environments (relying on simulation results)
◦
Further research is needed to determine the optimal parameters of the parameterized guess generator of PAC-MCoFL-p.
◦
Robustness verification is required for various network topologies and communication delays.
👍
Made with Slashpage