Quantized Analog Beamforming Enabled Multi-task Federated Learning Over-the-air
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866917964986449920 |
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| author | Yao, Jiacheng Xu, Wei Zhu, Guangxu Yang, Zhaohui Huang, Kaibin Niyato, Dusit |
| author_facet | Yao, Jiacheng Xu, Wei Zhu, Guangxu Yang, Zhaohui Huang, Kaibin Niyato, Dusit |
| contents | 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, a targeted analog beamforming method in closed form is proposed for statistical interference elimination. Analytical results reveal that the interference power vanishes by an order of $\mathcal{O}\left(1/N_r\right)$ with the number of analog phase shifters, $N_r$, 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_17649 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Quantized Analog Beamforming Enabled Multi-task Federated Learning Over-the-air Yao, Jiacheng Xu, Wei Zhu, Guangxu Yang, Zhaohui Huang, Kaibin Niyato, Dusit Information Theory Signal Processing 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, a targeted analog beamforming method in closed form is proposed for statistical interference elimination. Analytical results reveal that the interference power vanishes by an order of $\mathcal{O}\left(1/N_r\right)$ with the number of analog phase shifters, $N_r$, 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. |
| title | Quantized Analog Beamforming Enabled Multi-task Federated Learning Over-the-air |
| topic | Information Theory Signal Processing |
| url | https://arxiv.org/abs/2503.17649 |