mlsanityData to 7 Oct 2026

BEV-SGD: Best Effort Voting SGD for Analog Aggregation Based Federated Learning against Byzantine Attackers

arXiv 2110.09660 ↗First posted 18 Oct 2021cs.LG cs.CR cs.DCcited by 6

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Abstract (arXiv)

As a promising distributed learning technology, analog aggregation based federated learning over the air (FLOA) provides high communication efficiency and privacy provisioning under the edge computing paradigm. When all edge devices (workers) simultaneously upload their local updates to the parameter server (PS) through commonly shared time-frequency resources, the PS obtains the averaged update only rather than the individual local ones. While such a concurrent transmission and aggregation scheme reduces the latency and communication costs, it unfortunately renders FLOA vulnerable to Byzantine attacks. Aiming at Byzantine-resilient FLOA, this paper starts from analyzing the channel inversion (CI) mechanism that is widely used for power control in FLOA. Our theoretical analysis indicates that although CI can achieve good learning performance in the benign scenarios, it fails to work well with limited defensive capability against Byzantine attacks. Then, we propose a novel scheme called the best effort voting (BEV) power control policy that is integrated with stochastic gradient descent (SGD). Our BEV-SGD enhances the robustness of FLOA to Byzantine attacks, by allowing all the workers to send their local updates at their maximum transmit power. Under worst-case attacks, we derive the expected convergence rates of FLOA with CI and BEV power control policies, respectively. The rate comparison reveals that our BEV-SGD outperforms its counterpart with CI in terms of better convergence behavior, which is verified by experimental simulations.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 31 of its 57 references that are in the map. First posted on arXiv 18 Oct 2021.

Builds on

31 of 57 references.

References · 31

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  2. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  3. 2019Deep Leakage from Gradientscited by 1,004
  4. 2018Byzantine-Robust Distributed Learning: Towards Optimal Statistical Ratescited by 698
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  7. 2018The Hidden Vulnerability of Distributed Learning in Byzantiumcited by 324
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  10. 2018Federated Learning via Over-the-Air Computationcited by 215
  11. 2018Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)cited by 186
  12. 2019Federated Learning over Wireless Fading Channelscited by 178
19 more
  1. 2016QSGD: Communication-Efficient SGD via Gradient Quantization and Encodingcited by 147
  2. 2020One-Bit Over-the-Air Aggregation for Communication-Efficient Federated Edge Learning: Design and Convergence Analysiscited by 108
  3. 2018Cooperative SGD: A unified Framework for the Design and Analysis of Communication-Efficient SGD Algorithmscited by 106
  4. 2018LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed Learningcited by 95
  5. 2018Towards an Intelligent Edge: Wireless Communication Meets Machine Learningcited by 94
  6. 2019Federated Variance-Reduced Stochastic Gradient Descent with Robustness to Byzantine Attackscited by 79
  7. 2020Privacy For Free: Wireless Federated Learning Via Uncoded Transmission With Adaptive Power Controlcited by 62
  8. 2019Zeno++: Robust Fully Asynchronous SGDcited by 53
  9. 2021Learning Rate Optimization for Federated Learning Exploiting Over-the-air Computationcited by 27
  10. 2019Energy-Aware Analog Aggregation for Federated Learning with Redundant Datacited by 23
  11. 2020Fast Convergence Algorithm for Analog Federated Learningcited by 16
  12. 2021Edge Federated Learning Via Unit-Modulus Over-The-Air Computationcited by 16
  13. 2021Joint Optimization of Communications and Federated Learning Over the Aircited by 13
  14. 20211-Bit Compressive Sensing for Efficient Federated Learning Over the Aircited by 10
  15. 2019Collaborative Machine Learning at the Wireless Edge with Blind Transmitterscited by 9
  16. 2018Zeno: Distributed Stochastic Gradient Descent with Suspicion-based Fault-tolerancecited by 8
  17. 2019Secure Distributed On-Device Learning Networks With Byzantine Adversariescited by 7
  18. 2019Optimized Power Control for Over-the-Air Computation in Fading Channelscited by 7
  19. 2019Fast and Robust Distributed Learning in High Dimensioncited by 1

Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 2; 2024: 2; 2025: 1.

6 papers.

  1. 2023Over-The-Air Federated Learning: Status Quo, Open Challenges, and Future Directionscited by 21
  2. 2022A Survey on Over-the-Air Computationcited by 19
  3. 2023Spectrum Breathing: Protecting Over-the-Air Federated Learning Against Interferencecited by 9
  4. 2024Federated Learning Resilient to Byzantine Attacks and Data Heterogeneitycited by 4
  5. 2024Distributed Swarm Learning for Edge Internet of Thingscited by 4
  6. 2025Byzantine-Resilient Over-the-Air Federated Learning under Zero-Trust Architecturecited by 1