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 D evice S election and R esource A llocation for M ultiple F ederations G 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.

Shucun Fu

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