mlsanityData to 7 Oct 2026

Communication and Energy Efficient Wireless Federated Learning with Intrinsic Privacy

arXiv 2304.07460 ↗First posted 15 Apr 2023cs.LG cs.AI cs.CRcited by 5

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Abstract (arXiv)

Federated Learning (FL) is a collaborative learning framework that enables edge devices to collaboratively learn a global model while keeping raw data locally. Although FL avoids leaking direct information from local datasets, sensitive information can still be inferred from the shared models. To address the privacy issue in FL, differential privacy (DP) mechanisms are leveraged to provide formal privacy guarantee. However, when deploying FL at the wireless edge with over-the-air computation, ensuring client-level DP faces significant challenges. In this paper, we propose a novel wireless FL scheme called private federated edge learning with sparsification (PFELS) to provide client-level DP guarantee with intrinsic channel noise while reducing communication and energy overhead and improving model accuracy. The key idea of PFELS is for each device to first compress its model update and then adaptively design the transmit power of the compressed model update according to the wireless channel status without any artificial noise addition. We provide a privacy analysis for PFELS and prove the convergence of PFELS under general non-convex and non-IID settings. Experimental results show that compared with prior work, PFELS can improve the accuracy with the same DP guarantee and save communication and energy costs simultaneously.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 35 of its 59 references that are in the map. First posted on arXiv 15 Apr 2023.

Builds on

35 of 59 references.

References · 35

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2016Deep Learning with Differential Privacycited by 2,682
  4. 2016Optimization Methods for Large-Scale Machine Learningcited by 1,140
  5. 2020Adaptive Federated Optimizationcited by 1,036
  6. 2020Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimizationcited by 897
  7. 2019Federated Learning with Differential Privacy: Algorithms and Performance Analysiscited by 528
  8. 2018ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Modelscited by 475
  9. 2017Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Trainingcited by 471
  10. 2017Differentially Private Federated Learning: A Client Level Perspectivecited by 451
  11. 2020A Unified Theory of Decentralized SGD with Changing Topology and Local Updatescited by 361
  12. 2019Error Feedback Fixes SignSGD and other Gradient Compression Schemescited by 306
23 more
  1. 2018Privacy Amplification by Subsampling: Tight Analyses via Couplings and Divergencescited by 237
  2. 2018Federated Learning via Over-the-Air Computationcited by 215
  3. 2018Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)cited by 186
  4. 2019Federated Learning over Wireless Fading Channelscited by 178
  5. 2021The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregationcited by 127
  6. 2020Tempered Sigmoid Activations for Deep Learning with Differential Privacycited by 121
  7. 2019DoubleSqueeze: Parallel Stochastic Gradient Descent with Double-Pass Error-Compensated Compressioncited by 98
  8. 2020Privacy For Free: Wireless Federated Learning Via Uncoded Transmission With Adaptive Power Controlcited by 62
  9. 2019On Analog Gradient Descent Learning over Multiple Access Fading Channelscited by 61
  10. 2020Wireless Federated Learning with Local Differential Privacycited by 50
  11. 2020Energy-Efficient Resource Management for Federated Edge Learning with CPU-GPU Heterogeneous Computingcited by 39
  12. 2020FedSel: Federated SGD under Local Differential Privacy with Top-k Dimension Selectioncited by 35
  13. 2022Differentially Private Federated Learning with Local Regularization and Sparsificationcited by 35
  14. 2022Federated Learning with Sparsified Model Perturbation: Improving Accuracy under Client-Level Differential Privacycited by 32
  15. 2021Federated Edge Learning with Misaligned Over-The-Air Computationcited by 28
  16. 2020Federated Learning with Sparsification-Amplified Privacy and Adaptive Optimizationcited by 25
  17. 2020Harnessing Wireless Channels for Scalable and Privacy-Preserving Federated Learningcited by 24
  18. 2021Privacy Amplification for Federated Learning via User Sampling and Wireless Aggregationcited by 22
  19. 2021Towards Energy Efficient Federated Learning over 5G+ Mobile Devicescited by 16
  20. 2020Differentially Private AirComp Federated Learning with Power Adaptation Harnessing Receiver Noisecited by 14
  21. 2020Voting-based Approaches For Differentially Private Federated Learningcited by 14
  22. 2019Machine Learning at the Wireless Edge: Distributed Stochastic Gradient Descent Over-the-Aircited by 10
  23. 2021Wireless Federated Learning with Limited Communication and Differential Privacycited by 6

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Cited by year

Citing papers per year: 2024: 4; 2025: 1.

5 papers.

  1. 2024Heterogeneity-Aware Cooperative Federated Edge Learning with Adaptive Computation and Communication Compressioncited by 2
  2. 2024DP2-FedSAM: Enhancing Differentially Private Federated Learning Through Personalized Sharpness-Aware Minimizationcited by 0
  3. 2024Online Client Scheduling and Resource Allocation for Efficient Federated Edge Learningcited by 0
  4. 2024Providing Differential Privacy for Federated Learning Over Wireless: A Cross-layer Frameworkcited by 0
  5. 2025Free Privacy Protection for Wireless Federated Learning: Enjoy It or Suffer from It?cited by 0