One-Bit Aggregation for Over-the-Air Federated Learning Against Byzantine Attacks

To facilitate distributed machine learning in wireless networks, over-the-air federated learning (AirFL) is proposed to provide data privacy protection and high communication efficiency by leveraging the superposition property of wireless channels. However, as a typical parameter attack method, Byzantine attack brings challenges to the stable operation of AirFL systems. In this letter, we integrate orthogonal frequency division multiplexing and SignSGD with majority vote to enhance the resilience of AirFL against Byzantine attacks by performing one-bit gradient quantization. Theoretical analysis and numerical simulations are provided to validate the effectiveness of the proposed AirFL scheme under different channel states and Byzantine attacker percentages.