Joint Optimization of Device Selection and Resource Allocation for Multiple Federations in Federated Edge Learning
Shucun Fu, Fang Dong, Dian Shen, Jinghui Zhang, Zhaowu Huang, Qiang He
Abstract
Federated edge learning (FEEL) is a promising collaborative paradigm, which employs edge devices (EDs) to train machine learning models for a federation. It opens countless opportunities to enable edge intelligence. The increasingly diversified demands for intelligent services are driving the deployment of various federations at the edge. Existing works on FEEL focus on a single federation and ignore inter-federation device competition and intra-device resource allocation, which hinders the applications of FEEL. To address this issue, this article first investigates the bottlenecks of executing multiple federations and builds a joint optimization model as a two-stage Stackelberg game involving device selection and resource allocation. To tackle the problem efficiently, we present a game-theoretical approach named <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</u> evice <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</u> election and <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</u> esource <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</u> llocation for <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</u> ultiple <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</u> ederations <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</u> ame (DSRAMF-G). First, following the arbitrary device selection of leaders (i.e., federations), the time cost minimization of followers (i.e., EDs) is modeled as a convex problem to obtain the optimal resource allocation. Then, based on followers’ optimal responses, device selection is modeled as a congestion game. We prove the existence of the Nash equilibrium and propose a decentralized mechanism. Finally, extensive experiments show that DSRAMF-G significantly outperforms the state-of-the-art methods, achieving up to 5.9x training speedup and 2.8x resource-savings.