Learning-based Multi-Objective Resource Allocation for Over-the-Air Federated Learning

Over-the-air federated learning (AirFL) has developed as a communication-efficient solution to enable distributed machine learning over edge devices by integrating computation and communication into a joint design. However, the aggregation error caused by noise and wireless channel fading may lead to a compromised learning performance in an AirFL system. Potential malicious participants can also cause this compromise such that decoding the individual uploaded model updates at the server side to perform security examination is still imperative for AirFL. In this paper, we investigate a novel multi-objective resource allocation problem to combat against such aggregation error and improve the information transmission efficiency of uplink by jointly optimizing the device selection, power allocation and receive scalar control. The objective of the multi-objective optimization problem (MOP) is to minimize the average mean squared error (MSE) of the over-the-air aggregation for different communication rounds and maximize the long-term energy efficiency (EE) of the system. Considering the complexity and dynamic environment, we present a deep reinforcement learning (DRL) based framework to solve the MOP. The MOP is firstly decomposed into multiple subproblems. Each subproblem is modelled as a neural network, which can be addressed by the proposed learning-based resource allocation (LRA) algorithm. The numerical results illustrate that our proposed approach can effectively tackle the MOP and outperform the benchmark approaches with appropriate training mechanism and reward design.