WorkerFirst: Worker-Centric Model Selection for Federated Learning in Mobile Edge Computing

Federated Learning (FL) is viewed as a promising manner of distributed machine learning, because it leverages the rich local datasets of various participants while preserving their privacy. Particularly under the fifth-generation communications (5G) networks, FL shows its overwhelming advantages in the context of mobile edge computing (MEC). However, from the participant's viewpoint, a puzzle is how to guarantee the tradeoff between the profit brought by participating in FL training and the restriction of its battery capacity. Because communicating with the FL server and training an FL model locally are energy-hungry. To address such a puzzle, different from existing studies, we particularly formulate the model-selection problem from the standpoint of mobile participants (i.e., workers). We then exploit the framework of deep reinforcement learning (DRL) to reformulate a joint optimization for all FL participants, by considering the energy consumption, training timespan, and communication overheads of workers, simultaneously. To address the proposed worker-centric selection problem, we devised a double deep Q-learning Network (DDQN) algorithm and a deep Q-Learning (DQL) algorithm to strive for the adaptive model-selection decisions of each energy-sensitive participant under a varying MEC environment. The simulation results show that the proposed DDQN and DQL algorithms can quickly learn a good policy without knowing any prior knowledge of network conditions, and outperform other baselines.