Correlated Participation Decision Making for Federated Edge Learning

Driven by the sheer amount of data generated at the network edge and improved computation capabilities of mobile devices, federated edge learning (FEL) emerges as a novel paradigm to achieve edge intelligence with a favorable property of protecting privacy for data generators, i.e., edge devices. However, limited computation and communication resources at the edge make it challenging to execute FEL cost-efficiently in practice. Faced with this challenge, lots of existing work focus on the optimization control during the learning process. However, these research take no precaution in terms of composing the FEL system given heterogeneous candidate devices, which can severely impact the implementation performance. To solve this issue, we define a participation game to capture the dependent but competitive relationships among edge devices with respect to making decisions on whether to participate in a round of FEL. Then we propose a correlated equilibrium based participation decision making strategy to achieve individual rationality and global profit maximization at the same time, which can maintain the efficiency and sustainability of FEL in the long term. Furthermore, we devise an improved method with polynomial computational cost to enhance the scalability of the game-theoretic solution. The performance of our proposed scheme is evaluated through extensive experimental results.