mlsanityData to 7 Oct 2026

Personalizing Federated Learning with Over-the-Air Computations

arXiv 2302.12509 ↗First posted 24 Feb 2023cs.LGcited by 3

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Abstract (arXiv)

Federated edge learning is a promising technology to deploy intelligence at the edge of wireless networks in a privacy-preserving manner. Under such a setting, multiple clients collaboratively train a global generic model under the coordination of an edge server. But the training efficiency is often throttled by challenges arising from limited communication and data heterogeneity. This paper presents a distributed training paradigm that employs analog over-the-air computation to address the communication bottleneck. Additionally, we leverage a bi-level optimization framework to personalize the federated learning model so as to cope with the data heterogeneity issue. As a result, it enhances the generalization and robustness of each client's local model. We elaborate on the model training procedure and its advantages over conventional frameworks. We provide a convergence analysis that theoretically demonstrates the training efficiency. We also conduct extensive experiments to validate the efficacy of the proposed framework.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 14 of its 22 references that are in the map. First posted on arXiv 24 Feb 2023.

Builds on

14 of 22 references.

References · 14

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  4. 2017Federated Multi-Task Learningcited by 669
  5. 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 665
  6. 2021Exploiting Shared Representations for Personalized Federated Learningcited by 537
  7. 2021Towards Personalized Federated Learningcited by 462
  8. 2018Federated Learning via Over-the-Air Computationcited by 215
  9. 2019A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problemscited by 157
  10. 2019Scheduling Policies for Federated Learning in Wireless Networkscited by 131
  11. 2020Privacy For Free: Wireless Federated Learning Via Uncoded Transmission With Adaptive Power Controlcited by 62
  12. 2019On Analog Gradient Descent Learning over Multiple Access Fading Channelscited by 61
2 more
  1. 2022FedCorr: Multi-Stage Federated Learning for Label Noise Correctioncited by 61
  2. 2021Over-the-Air Aggregation for Federated Learning: Waveform Superposition and Prototype Validationcited by 9

Built on by

Cited by year

Citing papers per year: 2024: 3.

3 papers.

  1. 2024Adaptive Federated Learning Over the Aircited by 4
  2. 2024Pre-Training and Personalized Fine-Tuning via Over-the-Air Federated Meta-Learning: Convergence-Generalization Trade-Offscited by 3
  3. 2024Personalized Over-the-Air Federated Learning with Personalized Reconfigurable Intelligent Surfacescited by 0