Random Orthogonalization for Federated Learning in Massive MIMO Systems
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Abstract (arXiv)
We propose a novel uplink communication method, coined 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 model aggregation 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, while significantly reducing the channel estimation overhead at the receiver. Theoretical analyses with respect to both communication and machine learning performances 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. Cited by 13 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Jan 2022.
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Citing papers per year: 2022: 3; 2023: 3; 2024: 1; 2025: 3; 2026: 3.
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- 2022A Survey on Over-the-Air Computationcited by 19
- 2022Federated Learning and Meta Learning: Approaches, Applications, and Directionscited by 12
- 2022Over-the-Air Computation Based on Balanced Number Systems for Federated Edge Learningcited by 9
- 2023AirFL-Mem: Improving Communication-Learning Trade-Off by Long-Term Memorycited by 4
- 2024NCAirFL: CSI-Free Over-the-Air Federated Learning Based on Non-Coherent Detectioncited by 4
- 2025Federated Split Learning with Improved Communication and Storage Efficiencycited by 3
- 2023Digital Over-the-Air Federated Learning in Multi-Antenna Systemscited by 1
- 2023Differentially Private Wireless Federated Learning Using Orthogonal Sequencescited by 1
- 2025Rethinking Federated Learning Over the Air: The Blessing of Scaling Upcited by 1
- 2025Differential Privacy as a Perk: Federated Learning over Multiple-Access Fading Channels with a Multi-Antenna Base Stationcited by 1
- 2026Age-Aware Edge-Blind Federated Learning via Over-the-Air Aggregationcited by 1
- 2026Resource-Element Energy Difference for Noncoherent Over-the-Air Federated Learningcited by 0
- 2026Non-Coherent Over-the-Air Federated Learning: Protocol, Convergence, and Device Scheduling8 Sep 2026
- 2026Resource-Element Energy Difference for Noncoherent Over-the-Air Federated Learning8 May 2026
- 2026Age-Aware Edge-Blind Federated Learning via Over-the-Air Aggregation2 Feb 2026
- 2025Differential Privacy as a Perk: Federated Learning over Multiple-Access Fading Channels with a Multi-Antenna Base Station27 Oct 2025
- 2025Rethinking Federated Learning Over the Air: The Blessing of Scaling Up25 Aug 2025
- 2025Federated Split Learning with Improved Communication and Storage Efficiency21 Jul 2025
- 2024NCAirFL: CSI-Free Over-the-Air Federated Learning Based on Non-Coherent Detection20 Nov 2024
- 2023AirFL-Mem: Improving Communication-Learning Trade-Off by Long-Term Memory25 Oct 2023
- 2023Differentially Private Wireless Federated Learning Using Orthogonal Sequences14 Jun 2023
- 2023Digital Over-the-Air Federated Learning in Multi-Antenna Systems4 Feb 2023
- 2022Federated Learning and Meta Learning: Approaches, Applications, and Directions24 Oct 2022
- 2022A Survey on Over-the-Air Computation20 Oct 2022
- 2022A Survey on Over-the-Air Computationcited by 19
- 2022Federated Learning and Meta Learning: Approaches, Applications, and Directionscited by 12
- 2022Over-the-Air Computation Based on Balanced Number Systems for Federated Edge Learningcited by 9
- 2023AirFL-Mem: Improving Communication-Learning Trade-Off by Long-Term Memorycited by 4
- 2024NCAirFL: CSI-Free Over-the-Air Federated Learning Based on Non-Coherent Detectioncited by 4
- 2025Federated Split Learning with Improved Communication and Storage Efficiencycited by 3
- 2023Digital Over-the-Air Federated Learning in Multi-Antenna Systemscited by 1
- 2023Differentially Private Wireless Federated Learning Using Orthogonal Sequencescited by 1
- 2025Rethinking Federated Learning Over the Air: The Blessing of Scaling Upcited by 1
- 2025Differential Privacy as a Perk: Federated Learning over Multiple-Access Fading Channels with a Multi-Antenna Base Stationcited by 1
- 2026Age-Aware Edge-Blind Federated Learning via Over-the-Air Aggregationcited by 1
- 2026Resource-Element Energy Difference for Noncoherent Over-the-Air Federated Learningcited by 0
12 most cited of 13 All 13 (JSON)
- 2026Non-Coherent Over-the-Air Federated Learning: Protocol, Convergence, and Device Scheduling8 Sep 2026
- 2026Resource-Element Energy Difference for Noncoherent Over-the-Air Federated Learning8 May 2026
- 2026Age-Aware Edge-Blind Federated Learning via Over-the-Air Aggregation2 Feb 2026
- 2025Differential Privacy as a Perk: Federated Learning over Multiple-Access Fading Channels with a Multi-Antenna Base Station27 Oct 2025
- 2025Rethinking Federated Learning Over the Air: The Blessing of Scaling Up25 Aug 2025
- 2025Federated Split Learning with Improved Communication and Storage Efficiency21 Jul 2025
- 2024NCAirFL: CSI-Free Over-the-Air Federated Learning Based on Non-Coherent Detection20 Nov 2024
- 2023AirFL-Mem: Improving Communication-Learning Trade-Off by Long-Term Memory25 Oct 2023
- 2023Differentially Private Wireless Federated Learning Using Orthogonal Sequences14 Jun 2023
- 2023Digital Over-the-Air Federated Learning in Multi-Antenna Systems4 Feb 2023
- 2022Federated Learning and Meta Learning: Approaches, Applications, and Directions24 Oct 2022
- 2022A Survey on Over-the-Air Computation20 Oct 2022
12 newest of 13 All 13 (JSON)