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

arXiv 2503.17649 ↗First posted 22 Mar 2025cs.IT eess.SP math.ITcited by 0

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Abstract (arXiv)

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 Oleft(1/Nrright) 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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 11 of its 19 references that are in the map. First posted on arXiv 22 Mar 2025.

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References · 11

  1. 2020An Efficient Framework for Clustered Federated Learningcited by 511
  2. 2018Federated Learning via Over-the-Air Computationcited by 219
  3. 2019Federated Learning over Wireless Fading Channelscited by 183
  4. 2022Edge Learning for B5G Networks with Distributed Signal Processing: Semantic Communication, Edge Computing, and Wireless Sensingcited by 56
  5. 2021Turning Channel Noise into an Accelerator for Over-the-Air Principal Component Analysiscited by 14
  6. 2022Random Orthogonalization for Federated Learning in Massive MIMO Systemscited by 14
  7. 2021Over-the-Air Federated Multi-Task Learning Over MIMO Multiple Access Channelscited by 8
  8. 2023Imperfect CSI: A Key Factor of Uncertainty to Over-the-Air Federated Learningcited by 6
  9. 2025Combating Interference for Over-the-Air Federated Learning: A Statistical Approach via RIScited by 4
  10. 2024Empowering Over-the-Air Personalized Federated Learning via RIScited by 3
  11. 2022Secure Outage Analysis of RIS-Assisted Communications with Discrete Phase Controlcited by 1

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  1. 2023Edge Intelligence Over the Air: Two Faces of Interference in Federated Learning
  2. 2025Over-the-Air Federated Learning: Rethinking Edge AI Through Signal Processing
  3. 2026Non-Coherent Over-the-Air Federated Learning: Protocol, Convergence, and Device Scheduling
  4. 2024Analog-digital Scheduling for Federated Learning: A Communication-Efficient Approach
  5. 20211-Bit Compressive Sensing for Efficient Federated Learning Over the Air
  6. 2024Massive Digital Over-the-Air Computation for Communication-Efficient Federated Edge Learning
  7. 2022Over-the-Air Federated Learning via Second-Order Optimization
  8. 2023Channel and Gradient-Importance Aware Device Scheduling for Over-the-Air Federated Learning