mlsanityData to 7 Oct 2026

Robust Federated Learning via Over-The-Air Computation

arXiv 2111.01221 ↗First posted 1 Nov 2021cs.LGcited by 5

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Abstract (arXiv)

This paper investigates the robustness of over-the-air federated learning to Byzantine attacks. The simple averaging of the model updates via over-the-air computation makes the learning task vulnerable to random or intended modifications of the local model updates of some malicious clients. We propose a robust transmission and aggregation framework to such attacks while preserving the benefits of over-the-air computation for federated learning. For the proposed robust federated learning, the participating clients are randomly divided into groups and a transmission time slot is allocated to each group. The parameter server aggregates the results of the different groups using a robust aggregation technique and conveys the result to the clients for another training round. We also analyze the convergence of the proposed algorithm. Numerical simulations confirm the robustness of the proposed approach to Byzantine attacks.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 21 of its 31 references that are in the map. First posted on arXiv 1 Nov 2021.

Builds on

21 of 31 references.

References · 21

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019Advances and Open Problems in Federated Learningcited by 3,021
  3. 2018Byzantine-Robust Distributed Learning: Towards Optimal Statistical Ratescited by 698
  4. 2018Analyzing Federated Learning through an Adversarial Lenscited by 346
  5. 2019Robust Aggregation for Federated Learningcited by 314
  6. 2018Federated Learning via Over-the-Air Computationcited by 215
  7. 2019Federated Learning over Wireless Fading Channelscited by 178
  8. 2018RSA: Byzantine-Robust Stochastic Aggregation Methods for Distributed Learning from Heterogeneous Datasetscited by 148
  9. 2018D2: Decentralized Training over Decentralized Datacited by 140
  10. 2018Generalized Byzantine-tolerant SGDcited by 94
  11. 2019Federated Variance-Reduced Stochastic Gradient Descent with Robustness to Byzantine Attackscited by 79
  12. 2018DRACO: Byzantine-resilient Distributed Training via Redundant Gradientscited by 74
9 more
  1. 2020Over-the-Air Federated Learning from Heterogeneous Datacited by 72
  2. 2020Privacy For Free: Wireless Federated Learning Via Uncoded Transmission With Adaptive Power Controlcited by 62
  3. 2019On Analog Gradient Descent Learning over Multiple Access Fading Channelscited by 61
  4. 2017Distributed Statistical Machine Learning in Adversarial Settings: Byzantine Gradient Descentcited by 59
  5. 2019DETOX: A Redundancy-based Framework for Faster and More Robust Gradient Aggregationcited by 44
  6. 2018Defending Against Saddle Point Attack in Byzantine-Robust Distributed Learningcited by 32
  7. 2020Federated Learning and Wireless Communicationscited by 27
  8. 2013Local Privacy and Minimax Bounds: Sharp Rates for Probability Estimationcited by 19
  9. 2018The Internet of Things: Secure Distributed Inferencecited by 9

Built on by

Cited by year

Citing papers per year: 2022: 2; 2023: 1; 2024: 0; 2025: 2.

5 papers.

  1. 2023Over-the-Air Federated Learning and Optimizationcited by 10
  2. 2022Efficient Wireless Federated Learning with Partial Model Aggregationcited by 8
  3. 2022Over-The-Air Federated Learning Over Scalable Cell-free Massive MIMOcited by 2
  4. 2025Byzantine-Resilient Over-the-Air Federated Learning under Zero-Trust Architecturecited by 1
  5. 2025Robust Over-the-Air Computation with Type-Based Multiple Accesscited by 0