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Random Orthogonalization for Federated Learning in Massive MIMO Systems

arXiv 2210.09881 ↗First posted 18 Oct 2022cs.IT cs.LG eess.SPcited by 0

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Abstract (arXiv)

We propose a novel communication design, termed random orthogonalization, for federated learning (FL) in a massive multiple-input and multiple-output (MIMO) wireless system. The key novelty of random orthogonalization comes from the tight coupling of FL and two unique characteristics of massive MIMO -- channel hardening and favorable propagation. As a result, random orthogonalization can achieve natural over-the-air model aggregation without requiring transmitter side channel state information (CSI) for the uplink phase of FL, while significantly reducing the channel estimation overhead at the receiver. We extend this principle to the downlink communication phase and develop a simple but highly effective model broadcast method for FL. We also relax the massive MIMO assumption by proposing an enhanced random orthogonalization design for both uplink and downlink FL communications, that does not rely on channel hardening or favorable propagation. Theoretical analyses with respect to both communication and machine learning performance are carried out. In particular, an explicit relationship among the convergence rate, the number of clients, and the number of antennas is established. Experimental results validate the effectiveness and efficiency of random orthogonalization for FL in massive MIMO.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 37 of its 62 references that are in the map. First posted on arXiv 18 Oct 2022.

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37 of 62 references.

References · 37

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25 more
  1. 2020Convergence Time Optimization for Federated Learning over Wireless Networkscited by 81
  2. 2020Over-the-Air Federated Learning from Heterogeneous Datacited by 73
  3. 2015Perturbed Iterate Analysis for Asynchronous Stochastic Optimizationcited by 67
  4. 2019On Analog Gradient Descent Learning over Multiple Access Fading Channelscited by 62
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  12. 2021Convergence Analysis and System Design for Federated Learning over Wireless Networkscited by 25
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  14. 2021Federated Learning over Energy Harvesting Wireless Networkscited by 17
  15. 2021Adaptive Transmission Scheduling in Wireless Networks for Asynchronous Federated Learningcited by 15
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  20. 2021Over-the-Air Federated Multi-Task Learning Over MIMO Multiple Access Channelscited by 8
  21. 2022Over-the-Air Federated Learning with Energy Harvesting Devicescited by 8
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  23. 2022Time-Correlated Sparsification for Efficient Over-the-Air Model Aggregation in Wireless Federated Learningcited by 7
  24. 2020Adaptive Subcarrier, Parameter, and Power Allocation for Partitioned Edge Learning Over Broadband Channelscited by 5
  25. 2020A Compressive Sensing Approach for Federated Learning over Massive MIMO Communication Systemscited by 2

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