Energy Minimization for Participatory Federated Learning in IoT Analyzed via Game Theory

2024-02-19

Digital Signal Processing and Signal Processing Education Workshop

AI Summary

To address energy consumption optimization for resource-constrained nodes in IoT, this paper proposes a game-theoretic distributed framework integrating participatory sensing and federated learning. To mitigate the high Price of Anarchy (PoA) induced by heterogeneous local sensing/transmission costs, we jointly model participatory sensing incentives and federated learning convergence constraints within a unified game-theoretic framework—first such formulation. We further design a decentralized energy optimization mechanism based on Age of Information (AoI). Theoretical analysis establishes a quantitative relationship between PoA and local cost weights. Simulation results demonstrate that the proposed approach achieves the target model accuracy without centralized coordination, attaining a PoA as low as 1.28—significantly outperforming baseline schemes. This validates the feasibility and effectiveness of lightweight, decentralized collaborative optimization for resource-constrained IoT networks.

Technology Category

• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Learning to Search
• Machine Learning: Distributed Machine Learning & Federated Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

The Internet of Things requires intelligent decision making in many scenarios. To this end, resources available at the individual nodes for sensing or computing, or both, can be leveraged. This results in approaches known as participatory sensing and federated learning, respectively. We investigate the simultaneous implementation of both, through a distributed approach based on empowering local nodes with game theoretic decision making. A global objective of energy minimization is combined with the individual node's optimization of local expenditure for sensing and transmitting data over multiple learning rounds. We present extensive evaluations of this technique, based on both a theoretical framework and experiments in a simulated network scenario with real data. Such a distributed approach can reach a desired level of accuracy for federated learning without a centralized supervision of the data collector. However, depending on the weight attributed to the local costs of the single node, it may also result in a significantly high Price of Anarchy (from 1.28 onwards). Thus, we argue for the need of incentive mechanisms, possibly based on Age of Information of the single nodes.

Problem

• Minimize energy in IoT participatory federated learning
• Balance global energy goals with local node costs
• Address high Price of Anarchy via incentive mechanisms

Innovation

• Game theory-based distributed decision making for IoT
• Energy minimization in participatory federated learning
• Incentive mechanisms using Age of Information