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Random Aggregate Beamforming for Over-the-Air Federated Learning in Large-Scale Networks

arXiv 2403.18946 ↗First posted 20 Feb 2024cs.IT cs.AI cs.LGcited by 0

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Abstract (arXiv)

At present, there is a trend to deploy ubiquitous artificial intelligence (AI) applications at the edge of the network. As a promising framework that enables secure edge intelligence, federated learning (FL) has received widespread attention, and over-the-air computing (AirComp) has been integrated to further improve the communication efficiency. In this paper, we consider a joint device selection and aggregate beamforming design with the objectives of minimizing the aggregate error and maximizing the number of selected devices. This yields a combinatorial problem, which is difficult to solve especially in large-scale networks. To tackle the problems in a cost-effective manner, we propose a random aggregate beamforming-based scheme, which generates the aggregator beamforming vector via random sampling rather than optimization. The implementation of the proposed scheme does not require the channel estimation. We additionally use asymptotic analysis to study the obtained aggregate error and the number of the selected devices when the number of devices becomes large. Furthermore, a refined method that runs with multiple randomizations is also proposed for performance improvement. Extensive simulation results are presented to demonstrate the effectiveness of the proposed random aggregate beamforming-based scheme as well as the refined method.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 22 of its 40 references that are in the map. First posted on arXiv 20 Feb 2024.

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References · 22

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  12. 2019Energy Efficient Federated Learning Over Wireless Communication Networkscited by 181
10 more
  1. 2019Scheduling Policies for Federated Learning in Wireless Networkscited by 131
  2. 2021Distributed Learning in Wireless Networks: Recent Progress and Future Challengescited by 129
  3. 2018Parametric Noise Injection: Trainable Randomness to Improve Deep Neural Network Robustness against Adversarial Attackcited by 125
  4. 2018Towards an Intelligent Edge: Wireless Communication Meets Machine Learningcited by 95
  5. 2019Reliable Federated Learning for Mobile Networkscited by 92
  6. 2019Device Scheduling with Fast Convergence for Wireless Federated Learningcited by 46
  7. 2021Learning Rate Optimization for Federated Learning Exploiting Over-the-air Computationcited by 29
  8. 2018SmoothOut: Smoothing Out Sharp Minima to Improve Generalization in Deep Learningcited by 25
  9. 2021Noise Optimization for Artificial Neural Networkscited by 7
  10. 2020True-data Testbed for 5G/B5G Intelligent Networkcited by 5

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