How can I minimize energy in federated learning using game theory?
Energy Minimization for Participatory Federated Learning in IoT Analyzed via Game Theory
March 28, 2025
https://arxiv.org/pdf/2503.21722This paper explores energy-efficient federated learning (FL) in IoT networks using a game-theoretic approach. Individual devices (agents) decide probabilistically whether to participate in each training round, aiming to minimize a combination of training time and energy cost. This distributed approach, akin to a multi-agent system, allows local training on private data. However, individual selfishness can lead to suboptimal global performance (high Price of Anarchy). An incentive mechanism based on Age of Information (AoI) is introduced to encourage participation and improve overall energy efficiency.
Key points for LLM-based multi-agent systems:
- Decentralized decision-making: Agents (devices) make independent decisions about participation, reflecting the autonomy of agents in a multi-agent environment. LLMs could enhance this by enabling more sophisticated decision-making policies within each agent.
- Incentive mechanisms: AoI is used as an incentive to align individual agent behavior with global objectives. This relates to reward shaping in multi-agent reinforcement learning and can be adapted to LLM-based systems to guide agent behavior.
- Game-theoretic analysis: Analyzing participation as a game provides insights into the dynamics of multi-agent systems. This approach can be applied to LLM-based multi-agent scenarios to predict and optimize system behavior.
- Resource optimization: The focus on minimizing energy consumption is crucial in resource-constrained environments, a common concern in deploying multi-agent systems, especially with resource-intensive LLMs.
- Scalability: The distributed nature of FL is well-suited for large-scale multi-agent systems. LLMs can leverage this distributed paradigm to improve the scalability and efficiency of complex applications.