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

arXiv: https://arxiv.org/abs/2503.17649 · HTML: https://mlsanity.com/paper/2503.17649/

## 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.

## Builds on (most cited first, 11 of 11)

- [An Efficient Framework for Clustered Federated Learning](https://mlsanity.com/paper/2006.04088/) (2020)
- [Federated Learning via Over-the-Air Computation](https://mlsanity.com/paper/1812.11750/) (2018)
- [Federated Learning over Wireless Fading Channels](https://mlsanity.com/paper/1907.09769/) (2019)
- [Edge Learning for B5G Networks with Distributed Signal Processing: Semantic Communication, Edge Computing, and Wireless Sensing](https://mlsanity.com/paper/2206.00422/) (2022)
- [Turning Channel Noise into an Accelerator for Over-the-Air Principal Component Analysis](https://mlsanity.com/paper/2104.10095/) (2021)
- [Random Orthogonalization for Federated Learning in Massive MIMO Systems](https://mlsanity.com/paper/2201.12490/) (2022)
- [Over-the-Air Federated Multi-Task Learning Over MIMO Multiple Access Channels](https://mlsanity.com/paper/2112.13603/) (2021)
- [Imperfect CSI: A Key Factor of Uncertainty to Over-the-Air Federated Learning](https://mlsanity.com/paper/2307.12793/) (2023)
- [Combating Interference for Over-the-Air Federated Learning: A Statistical Approach via RIS](https://mlsanity.com/paper/2501.16081/) (2025)
- [Empowering Over-the-Air Personalized Federated Learning via RIS](https://mlsanity.com/paper/2408.12162/) (2024)
- [Secure Outage Analysis of RIS-Assisted Communications with Discrete Phase Control](https://mlsanity.com/paper/2210.17084/) (2022)

## 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.