Multi-Task Federated Learning with Over-the-Air Computation for MIMO Interference Channels
Chenxi Zhong, Huiyuan Yang, Xiaojun Yuan · 2022 International Symposium on Networks, Computers and Communications (ISNCC) · 2022
Although Federated learning (FL) over wireless medium is a promising technology, a large number of concurrent FL tasks, generated by the urgent demand for ubiquitous intelligence, may seriously aggravate the scarcity of communication resources. By exploiting the analog superposition of electromagnetic waves, over-the-air computation (AirComp) is an appealing solution to alleviate the burden of communication required by FL. However, sharing frequency-time resources in AirComp inevitably brings about the problem of inter-task interference, which poses a new challenge. In this paper, we study over-the-air multi-task FL (OA-MTFL) over the multiple-input multiple-output (MIMO) interference channel. We establish a communication-learning analytical framework for the proposed OA-MTFL scheme by considering the spatial correlation between devices, formulate an optimization problem of designing transceiver beamforming and device selection, and develop an efficient algorithm to solve it. The numerical results demonstrate the outstanding performance of the proposed scheme.