mlsanityData to 7 Oct 2026

Boosting Fairness and Robustness in Over-the-Air Federated Learning

arXiv 2403.04431 ↗First posted 7 Mar 2024cs.LG cs.CYcited by 1

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Abstract (arXiv)

Over-the-Air Computation is a beyond-5G communication strategy that has recently been shown to be useful for the decentralized training of machine learning models due to its efficiency. In this paper, we propose an Over-the-Air federated learning algorithm that aims to provide fairness and robustness through minmax optimization. By using the epigraph form of the problem at hand, we show that the proposed algorithm converges to the optimal solution of the minmax problem. Moreover, the proposed approach does not require reconstructing channel coefficients by complex encoding-decoding schemes as opposed to state-of-the-art approaches. This improves both efficiency and privacy.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 16 of its 34 references that are in the map. First posted on arXiv 7 Mar 2024.

Builds on

16 of 34 references.

References · 16

  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. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  4. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  5. 2019Towards Federated Learning at Scale: System Designcited by 826
  6. 2017Federated Multi-Task Learningcited by 669
  7. 2019Agnostic Federated Learningcited by 451
  8. 2018Federated Learning via Over-the-Air Computationcited by 215
  9. 2023Heterogeneous Federated Learning: State-of-the-art and Research Challengescited by 160
  10. 2020Over-the-Air Federated Learning from Heterogeneous Datacited by 72
  11. 2020Federated Learning Meets Multi-objective Optimizationcited by 50
  12. 2021Federated Learning: A Signal Processing Perspectivecited by 49
4 more
  1. 2023On the Convergence of Decentralized Federated Learning Under Imperfect Information Sharingcited by 16
  2. 2021Over-The-Air Computation in Correlated Channelscited by 5
  3. 2020Max-Consensus Over Fading Wireless Channelscited by 3
  4. 2023Federated Learning in Wireless Networks via Over-the-Air Computationscited by 2

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Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025Fairness in Federated Learning: Fairness for Whom?cited by 1