Solving the Federated Edge Learning Participation Dilemma: A Truthful and Correlated Perspective

TL;DR

This paper addresses the challenge of incentivizing edge devices to participate in federated edge learning by using game theory and mechanism design to ensure truthful information sharing and stable participation, improving overall system performance.

Contribution

It introduces a game-theoretic framework with mechanism design and correlated equilibrium to promote truthful participation in FEL considering data size heterogeneity.

Findings

Achieves long-term stability and efficacy in FEL participation

Reduces computational complexity to polynomial level

Demonstrates performance improvements through extensive experiments

Abstract

An emerging computational paradigm, named federated edge learning (FEL), enables intelligent computing at the network edge with the feature of preserving data privacy for edge devices. Given their constrained resources, it becomes a great challenge to achieve high execution performance for FEL. Most of the state-of-the-arts concentrate on enhancing FEL from the perspective of system operation procedures, taking few precautions during the composition step of the FEL system. Though a few recent studies recognize the importance of FEL formation and propose server-centric device selection schemes, the impact of data sizes is largely overlooked. In this paper, we take advantage of game theory to depict the decision dilemma among edge devices regarding whether to participate in FEL or not given their heterogeneous sizes of local datasets. For realizing both the individual and global optimization, the server is employed to solve the participation dilemma, which requires accurate information collection for devices' local datasets. Hence, we utilize mechanism design to enable truthful information solicitation. With the help of correlated equilibrium, we derive a decision making strategy for devices from the global perspective, which can achieve the long-term stability and efficacy of FEL. For scalability consideration, we optimize the computational complexity of the basic solution to the polynomial level. Lastly, extensive experiments based on both real and synthetic data are conducted to evaluate our proposed mechanisms, with experimental results demonstrating the performance advantages.