Over-the-Air Federated Multi-Task Learning Over MIMO Multiple Access Channels
نویسندگان
چکیده
With the explosive growth of data and wireless devices, federated learning (FL) over medium has emerged as a promising technology for large-scale distributed intelligent systems. Yet, urgent demand ubiquitous intelligence will generate large number concurrent FL tasks, which may seriously aggravate scarcity communication resources. By exploiting analog superposition electromagnetic waves, over-the-air computation (AirComp) is an appealing solution to alleviate burden required by FL. However, sharing frequency-time resources in inevitably brings about problem inter-task interference, poses new challenge that needs be appropriately addressed. In this paper, we study multi-task (OA-FMTL) multiple-input multiple-output (MIMO) multiple access (MAC) channel. We propose novel model aggregation method alignment local gradients different alleviates straggler due channel heterogeneity. establish communication-learning analysis framework proposed OA-FMTL scheme considering spatial correlation between formulate optimization design transceiver beamforming device selection. To solve problem, develop algorithm using alternating (AO) fractional programming (FP), effectively mitigates impact interference on performance. show use method, selection no longer essential, thereby avoiding heavy computational involved selecting active devices. Numerical results demonstrate validity superb performance scheme.
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ژورنال
عنوان ژورنال: IEEE Transactions on Wireless Communications
سال: 2023
ISSN: ['1536-1276', '1558-2248']
DOI: https://doi.org/10.1109/twc.2022.3221797