mlsanityData to 7 Oct 2026

FedEM: A Privacy-Preserving Framework for Concurrent Utility Preservation in Federated Learning

arXiv 2503.06021 ↗First posted 8 Mar 2025cs.LGcited by 0

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Abstract (arXiv)

Federated Learning (FL) enables collaborative training of models across distributed clients without sharing local data, addressing privacy concerns in decentralized systems. However, the gradient-sharing process exposes private data to potential leakage, compromising FL's privacy guarantees in real-world applications. To address this issue, we propose Federated Error Minimization (FedEM), a novel algorithm that incorporates controlled perturbations through adaptive noise injection. This mechanism effectively mitigates gradient leakage attacks while maintaining model performance. Experimental results on benchmark datasets demonstrate that FedEM significantly reduces privacy risks and preserves model accuracy, achieving a robust balance between privacy protection and utility preservation.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 27 of its 47 references that are in the map. First posted on arXiv 8 Mar 2025.

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27 of 47 references.

References · 27

  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. 2019Deep Leakage from Gradientscited by 1,004
  4. 2020Inverting Gradients -- How easy is it to break privacy in federated learning?cited by 707
  5. 2018Exploiting Unintended Feature Leakage in Collaborative Learningcited by 509
  6. 2018ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Modelscited by 475
  7. 2017Differentially Private Federated Learning: A Client Level Perspectivecited by 451
  8. 2020iDLG: Improved Deep Leakage from Gradientscited by 367
  9. 2021See through Gradients: Image Batch Recovery via GradInversioncited by 320
  10. 2020Systematic Evaluation of Privacy Risks of Machine Learning Modelscited by 273
  11. 2021Unlearnable Examples: Making Personal Data Unexploitablecited by 188
  12. 2021When the Curious Abandon Honesty: Federated Learning Is Not Privatecited by 126
15 more
  1. 2021Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Modelscited by 106
  2. 2020R-GAP: Recursive Gradient Attack on Privacycited by 95
  3. 2020Local Differential Privacy based Federated Learning for Internet of Thingscited by 74
  4. 2021Practical Blind Membership Inference Attack via Differential Comparisonscited by 73
  5. 2020LDP-FL: Practical Private Aggregation in Federated Learning with Local Differential Privacycited by 63
  6. 2022Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Modelscited by 52
  7. 2022No Free Lunch Theorem for Security and Utility in Federated Learningcited by 39
  8. 2021Federated Learning with Local Differential Privacy: Trade-offs between Privacy, Utility, and Communicationcited by 38
  9. 2022Gradient Obfuscation Gives a False Sense of Security in Federated Learningcited by 37
  10. 2020FedSel: Federated SGD under Local Differential Privacy with Top-k Dimension Selectioncited by 35
  11. 2021Preventing Unauthorized Use of Proprietary Data: Poisoning for Secure Dataset Releasecited by 35
  12. 2021Understanding Training-Data Leakage from Gradients in Neural Networks for Image Classificationcited by 16
  13. 2023Theoretically Principled Federated Learning for Balancing Privacy and Utilitycited by 6
  14. 2023A Meta-learning Framework for Tuning Parameters of Protection Mechanisms in Trustworthy Federated Learningcited by 6
  15. 2023Probably Approximately Correct Federated Learningcited by 4

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