Quantized Analog Beamforming Enabled Multi-task Federated Learning Over-the-air

Over-the-air computation (AirComp) has recently emerged as a pivotal technique for communication-efficient federated learning (FL) in resource-constrained wireless networks. Though AirComp leverages the superposition property of multiple access channels for computation, it inherently limits its ability to manage inter-task interference in multi-task computing. In this paper, we propose a quantized analog beamforming scheme at the receiver to enable simultaneous multi-task FL. Specifically, inspiring by the favorable propagation and channel hardening properties of large-scale antenna arrays, we propose an analog beamforming method in closed form for statistical interference elimination. Analytical results reveal that the interference power vanishes by an order of O (1/Nr) with the number of analog phase shifters, Nr, irrespective of their quantization precision. Numerical results demonstrate the effectiveness of the proposed analog beamforming method and show that the performance upper bound of ideal learning without errors can be achieved by increasing the number of low-precision analog phase shifters.

Quantized Analog Beamforming Enabled Multi-task Federated Learning Over-the-air

Jiacheng Yao, Wei Xu, Southeast University; Guangxu Zhu, Shenzhen Research Institute of Big Data; Zhaohui Yang, Zhejiang University; Kaibin Huang, The University of Hong Kong; Dusit Niyato, Nanyang Technological University