mlsanityData to 14 Oct 2026

ParaAegis: Parallel Protection for Flexible Privacy-preserved Federated Learning

arXiv 2509.13739 ↗First posted 17 Sep 2025cs.LG cs.DCcited by 0

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Abstract (arXiv)

Federated learning (FL) faces a critical dilemma: existing protection mechanisms like differential privacy (DP) and homomorphic encryption (HE) enforce a rigid trade-off, forcing a choice between model utility and computational efficiency. This lack of flexibility hinders the practical implementation. To address this, we introduce ParaAegis, a parallel protection framework designed to give practitioners flexible control over the privacy-utility-efficiency balance. Our core innovation is a strategic model partitioning scheme. By applying lightweight DP to the less critical, low norm portion of the model while protecting the remainder with HE, we create a tunable system. A distributed voting mechanism ensures consensus on this partitioning. Theoretical analysis confirms the adjustments between efficiency and utility with the same privacy. Crucially, the experimental results demonstrate that by adjusting the hyperparameters, our method enables flexible prioritization between model accuracy and training time.

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13 of 25 references.

References · 13

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  3. 2016Deep Learning with Differential Privacycited by 2,728
  4. 2018Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examplescited by 1,268
  5. 2020Adaptive Federated Optimizationcited by 1,046
  6. 2020Inverting Gradients -- How easy is it to break privacy in federated learning?cited by 712
  7. 2019Federated Learning with Differential Privacy: Algorithms and Performance Analysiscited by 536
  8. 2021Understanding Clipping for Federated Learning: Convergence and Client-Level Differential Privacycited by 69
  9. 2021Privacy-preserving Federated Learning based on Multi-key Homomorphic Encryptioncited by 48
  10. 2020Achieving Security and Privacy in Federated Learning Systems: Survey, Research Challenges and Future Directionscited by 25
  11. 2021Efficient Differentially Private Secure Aggregation for Federated Learning via Hardness of Learning with Errorscited by 25
  12. 2023Amplitude-Varying Perturbation for Balancing Privacy and Utility in Federated Learningcited by 14
1 more
  1. 2024Clients Collaborate: Flexible Differentially Private Federated Learning with Guaranteed Improvement of Utility-Privacy Trade-offcited by 7

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