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Personalized Over-the-Air Federated Learning with Personalized Reconfigurable Intelligent Surfaces

arXiv 2401.12149 ↗First posted 22 Jan 2024cs.IT cs.LG math.ITcited by 0

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Abstract (arXiv)

Over-the-air federated learning (OTA-FL) provides bandwidth-efficient learning by leveraging the inherent superposition property of wireless channels. Personalized federated learning balances performance for users with diverse datasets, addressing real-life data heterogeneity. We propose the first personalized OTA-FL scheme through multi-task learning, assisted by personal reconfigurable intelligent surfaces (RIS) for each user. We take a cross-layer approach that optimizes communication and computation resources for global and personalized tasks in time-varying channels with imperfect channel state information, using multi-task learning for non-i.i.d data. Our PROAR-PFed algorithm adaptively designs power, local iterations, and RIS configurations. We present convergence analysis for non-convex objectives and demonstrate that PROAR-PFed outperforms state-of-the-art on the Fashion-MNIST dataset.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 16 of its 23 references that are in the map. First posted on arXiv 22 Jan 2024.

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References · 16

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,283
  3. 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 665
  4. 2021Federated Learning on Non-IID Data Silos: An Experimental Studycited by 566
  5. 2020One-Bit Over-the-Air Aggregation for Communication-Efficient Federated Edge Learning: Design and Convergence Analysiscited by 108
  6. 2018Intelligent Reflecting Surface Enhanced Wireless Network via Joint Active and Passive Beamformingcited by 41
  7. 2020Federated Learning via Intelligent Reflecting Surfacecited by 38
  8. 2020Reconfigurable Intelligent Surface Enabled Federated Learning: A Unified Communication-Learning Design Approachcited by 32
  9. 2019Wireless Communications with Reconfigurable Intelligent Surface: Path Loss Modeling and Experimental Measurementcited by 8
  10. 2022Over-the-Air Federated Learning with Joint Adaptive Computation and Power Controlcited by 8
  11. 2020Federated Learning in Multi-RIS Aided Systemscited by 7
  12. 2022CHARLES: Channel-Quality-Adaptive Over-the-Air Federated Learning over Wireless Networkscited by 6
4 more
  1. 2023Personalizing Federated Learning with Over-the-Air Computationscited by 3
  2. 2023ROAR-Fed: RIS-Assisted Over-the-Air Adaptive Resource Allocation for Federated Learningcited by 2
  3. 2023RIS-Assisted Over-the-Air Adaptive Federated Learning with Noisy Downlinkcited by 2
  4. 2022Iterative Power Control for Wireless Networks with Distributed Reconfigurable Intelligent Surfacescited by 1

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