mlsanityData to 7 Oct 2026

Over-the-Air Federated Learning via Second-Order Optimization

arXiv 2203.15488 ↗First posted 29 Mar 2022cs.IT cs.LG eess.SPcited by 11

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Abstract (arXiv)

Federated learning (FL) is a promising learning paradigm that can tackle the increasingly prominent isolated data islands problem while keeping users' data locally with privacy and security guarantees. However, FL could result in task-oriented data traffic flows over wireless networks with limited radio resources. To design communication-efficient FL, most of the existing studies employ the first-order federated optimization approach that has a slow convergence rate. This however results in excessive communication rounds for local model updates between the edge devices and edge server. To address this issue, in this paper, we instead propose a novel over-the-air second-order federated optimization algorithm to simultaneously reduce the communication rounds and enable low-latency global model aggregation. This is achieved by exploiting the waveform superposition property of a multi-access channel to implement the distributed second-order optimization algorithm over wireless networks. The convergence behavior of the proposed algorithm is further characterized, which reveals a linear-quadratic convergence rate with an accumulative error term in each iteration. We thus propose a system optimization approach to minimize the accumulated error gap by joint device selection and beamforming design. Numerical results demonstrate the system and communication efficiency compared with the state-of-the-art approaches.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 42 of its 66 references that are in the map. First posted on arXiv 29 Mar 2022.

Builds on

42 of 66 references.

References · 42

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019Towards Federated Learning at Scale: System Designcited by 826
  3. 2018signSGD: Compressed Optimisation for Non-Convex Problemscited by 561
  4. 2016Federated Optimization: Distributed Machine Learning for On-Device Intelligencecited by 558
  5. 2021A Field Guide to Federated Optimizationcited by 312
  6. 2014Sketching as a Tool for Numerical Linear Algebracited by 285
  7. 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 262
  8. 2017Sparse Communication for Distributed Gradient Descentcited by 243
  9. 2019PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimizationcited by 234
  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
30 more
  1. 2020Is Local SGD Better than Minibatch SGD?cited by 151
  2. 2019The Roadmap to 6G -- AI Empowered Wireless Networkscited by 150
  3. 2013Communication Efficient Distributed Optimization using an Approximate Newton-type Methodcited by 131
  4. 2020One-Bit Over-the-Air Aggregation for Communication-Efficient Federated Edge Learning: Design and Convergence Analysiscited by 108
  5. 2020FedSplit: An algorithmic framework for fast federated optimizationcited by 108
  6. 2020Communication-Efficient Edge AI: Algorithms and Systemscited by 95
  7. 2020Federated Accelerated Stochastic Gradient Descentcited by 85
  8. 2019ZO-AdaMM: Zeroth-Order Adaptive Momentum Method for Black-Box Optimizationcited by 75
  9. 2016CoCoA: A General Framework for Communication-Efficient Distributed Optimizationcited by 69
  10. 2020Privacy For Free: Wireless Federated Learning Via Uncoded Transmission With Adaptive Power Controlcited by 62
  11. 2019On Analog Gradient Descent Learning over Multiple Access Fading Channelscited by 61
  12. 2020Wireless Federated Learning with Local Differential Privacycited by 50
  13. 2021Optimized Power Control Design for Over-the-Air Federated Edge Learningcited by 45
  14. 2017GIANT: Globally Improved Approximate Newton Method for Distributed Optimizationcited by 43
  15. 2020Federated Learning via Intelligent Reflecting Surfacecited by 38
  16. 2020Reconfigurable Intelligent Surface Enabled Federated Learning: A Unified Communication-Learning Design Approachcited by 32
  17. 2021Learning Rate Optimization for Federated Learning Exploiting Over-the-air Computationcited by 27
  18. 2020Harnessing Wireless Channels for Scalable and Privacy-Preserving Federated Learningcited by 24
  19. 2020Federated Machine Learning for Intelligent IoT via Reconfigurable Intelligent Surfacecited by 21
  20. 2019DINGO: Distributed Newton-Type Method for Gradient-Norm Optimizationcited by 20
  21. 2020Distributed Newton Can Communicate Less and Resist Byzantine Workerscited by 20
  22. 2020Fast Convergence Algorithm for Analog Federated Learningcited by 16
  23. 2018Convergence guarantees for a class of non-convex and non-smooth optimization problemscited by 15
  24. 2021Joint Optimization of Communications and Federated Learning Over the Aircited by 13
  25. 2021Over-the-Air Computation via Reconfigurable Intelligent Surfacecited by 12
  26. 2020Communication Efficient Federated Learning over Multiple Access Channelscited by 11
  27. 2021Delay Analysis of Wireless Federated Learning Based on Saddle Point Approximation and Large Deviation Theorycited by 9
  28. 2021On Second-order Optimization Methods for Federated Learningcited by 9
  29. 2021Over-the-Air Aggregation for Federated Learning: Waveform Superposition and Prototype Validationcited by 9
  30. 2020DINO: Distributed Newton-Type Optimization Methodcited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 5; 2024: 2; 2025: 3; 2026: 1.

11 papers.

  1. 2023FedSpeed: Larger Local Interval, Less Communication Round, and Higher Generalization Accuracycited by 49
  2. 2023Over-the-Air Federated Learning and Optimizationcited by 10
  3. 2023Joint Age-based Client Selection and Resource Allocation for Communication-Efficient Federated Learning over NOMA Networkscited by 8
  4. 2023Blind Federated Learning via Over-the-Air q-QAMcited by 7
  5. 2024Adaptive Federated Learning Over the Aircited by 4
  6. 2025Over-the-Air Fair Federated Learning via Multi-Objective Optimizationcited by 3
  7. 2025Heterogeneity-aware Personalized Federated Learning via Adaptive Dual-Agent Reinforcement Learningcited by 2
  8. 2024GP-FL: Model-Based Hessian Estimation for Second-Order Over-the-Air Federated Learningcited by 1
  9. 2023Federated Linear Bandit Learning via Over-the-Air Computationcited by 0
  10. 2025Accelerated Training of Federated Learning via Second-Order Methodscited by 0
  11. 2026AirGC-CD: Gaussian-Circulant Precoding for Exactly Debiasable PAPR Reduction in Over-the-Air Federated Learningcited by 0