mlsanityData to 7 Oct 2026

Differentially Private Federated Learning via Reconfigurable Intelligent Surface

arXiv 2203.17028 ↗First posted 31 Mar 2022eess.SP cs.CR cs.ITcited by 5

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Abstract (arXiv)

Federated learning (FL), as a disruptive machine learning paradigm, enables the collaborative training of a global model over decentralized local datasets without sharing them. It spans a wide scope of applications from Internet-of-Things (IoT) to biomedical engineering and drug discovery. To support low-latency and high-privacy FL over wireless networks, in this paper, we propose a reconfigurable intelligent surface (RIS) empowered over-the-air FL system to alleviate the dilemma between learning accuracy and privacy. This is achieved by simultaneously exploiting the channel propagation reconfigurability with RIS for boosting the receive signal power, as well as waveform superposition property with over-the-air computation (AirComp) for fast model aggregation. By considering a practical scenario where high-dimensional local model updates are transmitted across multiple communication blocks, we characterize the convergence behaviors of the differentially private federated optimization algorithm. We further formulate a system optimization problem to optimize the learning accuracy while satisfying privacy and power constraints via the joint design of transmit power, artificial noise, and phase shifts at RIS, for which a two-step alternating minimization framework is developed. Simulation results validate our systematic, theoretical, and algorithmic achievements and demonstrate that RIS can achieve a better trade-off between privacy and accuracy for over-the-air FL systems.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 37 of its 66 references that are in the map. First posted on arXiv 31 Mar 2022.

Builds on

37 of 66 references.

References · 37

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  3. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,220
  4. 2019Deep Leakage from Gradientscited by 1,004
  5. 2020Inverting Gradients -- How easy is it to break privacy in federated learning?cited by 707
  6. 2020The Future of Digital Health with Federated Learningcited by 625
  7. 2017Deep Models Under the GAN: Information Leakage from Collaborative Deep Learningcited by 326
  8. 2019Differential Privacy Has Disparate Impact on Model Accuracycited by 316
  9. 2021A Field Guide to Federated Optimizationcited by 312
  10. 2021Federated Learning for Internet of Things: A Comprehensive Surveycited by 247
  11. 2018Federated Learning via Over-the-Air Computationcited by 215
  12. 2018Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)cited by 186
25 more
  1. 2018cpSGD: Communication-efficient and differentially-private distributed SGDcited by 157
  2. 2019The Roadmap to 6G -- AI Empowered Wireless Networkscited by 150
  3. 2020Understanding Gradient Clipping in Private SGD: A Geometric Perspectivecited by 140
  4. 2021Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applicationscited by 96
  5. 2020Communication-Efficient Edge AI: Algorithms and Systemscited by 95
  6. 2020Privacy For Free: Wireless Federated Learning Via Uncoded Transmission With Adaptive Power Controlcited by 62
  7. 2019On Analog Gradient Descent Learning over Multiple Access Fading Channelscited by 61
  8. 2020Wireless Federated Learning with Local Differential Privacycited by 50
  9. 2018Intelligent Reflecting Surface Enhanced Wireless Network via Joint Active and Passive Beamformingcited by 41
  10. 2020Federated Learning via Intelligent Reflecting Surfacecited by 38
  11. 2020Reconfigurable Intelligent Surface Enabled Federated Learning: A Unified Communication-Learning Design Approachcited by 32
  12. 2020Smart Radio Environments Empowered by Reconfigurable Intelligent Surfaces: How it Works, State of Research, and Road Aheadcited by 26
  13. 2019The Value of Collaboration in Convex Machine Learning with Differential Privacycited by 24
  14. 2021Privacy Amplification for Federated Learning via User Sampling and Wireless Aggregationcited by 22
  15. 2020Federated Machine Learning for Intelligent IoT via Reconfigurable Intelligent Surfacecited by 21
  16. 2020Massive MIMO Channel Prediction: Kalman Filtering vs. Machine Learningcited by 21
  17. 2020Private Wireless Federated Learning with Anonymous Over-the-Air Computationcited by 16
  18. 2020Shuffled Model of Federated Learning: Privacy, Communication and Accuracy Trade-offscited by 15
  19. 2020Differentially Private AirComp Federated Learning with Power Adaptation Harnessing Receiver Noisecited by 14
  20. 2021Private Retrieval, Computing and Learning: Recent Progress and Future Challengescited by 12
  21. 2019Robust and Secure Wireless Communications via Intelligent Reflecting Surfacescited by 9
  22. 2021Delay Analysis of Wireless Federated Learning Based on Saddle Point Approximation and Large Deviation Theorycited by 9
  23. 2021Edge Intelligence for Empowering IoT-based Healthcare Systemscited by 8
  24. 2020Deep Multimodal Learning: Merging Sensory Data for Massive MIMO Channel Predictioncited by 7
  25. 2021Privacy-Preserving Wireless Federated Learning Exploiting Inherent Hardware Impairmentscited by 2

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Cited by year

Citing papers per year: 2022: 2; 2023: 3.

5 papers.

  1. 2023Machine Learning for Large-Scale Optimization in 6G Wireless Networkscited by 18
  2. 2023Over-the-Air Federated Learning and Optimizationcited by 10
  3. 2022Over-the-Air Federated Learning with Privacy Protection via Correlated Additive Perturbationscited by 5
  4. 2023Differentially Private Over-the-Air Federated Learning Over MIMO Fading Channelscited by 5
  5. 2022Federated Reinforcement Learning for Real-Time Electric Vehicle Charging and Discharging Controlcited by 4