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A Privacy-Centric Approach: Scalable and Secure Federated Learning Enabled by Hybrid Homomorphic Encryption

arXiv 2507.14853 ↗First posted 20 Jul 2025cs.CR cs.LGcited by 0

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Abstract (arXiv)

Federated Learning (FL) enables collaborative model training without sharing raw data, making it a promising approach for privacy-sensitive domains. Despite its potential, FL faces significant challenges, particularly in terms of communication overhead and data privacy. Privacy-preserving Techniques (PPTs) such as Homomorphic Encryption (HE) have been used to mitigate these concerns. However, these techniques introduce substantial computational and communication costs, limiting their practical deployment. In this work, we explore how Hybrid Homomorphic Encryption (HHE), a cryptographic protocol that combines symmetric encryption with HE, can be effectively integrated with FL to address both communication and privacy challenges, paving the way for scalable and secure decentralized learning system.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 15 of its 40 references that are in the map. First posted on arXiv 20 Jul 2025.

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References · 15

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,475
  3. 2019Deep Leakage from Gradientscited by 1,021
  4. 2020Inverting Gradients -- How easy is it to break privacy in federated learning?cited by 712
  5. 2019A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protectioncited by 374
  6. 2021Privacy-Preserving Machine Learning with Fully Homomorphic Encryption for Deep Neural Networkcited by 57
  7. 2020ARIANN: Low-Interaction Privacy-Preserving Deep Learning via Function Secret Sharingcited by 20
  8. 2021Secure Neuroimaging Analysis using Federated Learning with Homomorphic Encryptioncited by 18
  9. 2020PrivFL: Practical Privacy-preserving Federated Regressions on High-dimensional Data over Mobile Networkscited by 10
  10. 2023Love or Hate? Share or Split? Privacy-Preserving Training Using Split Learning and Homomorphic Encryptioncited by 8
  11. 2024GuardML: Efficient Privacy-Preserving Machine Learning Services Through Hybrid Homomorphic Encryptioncited by 7
  12. 2023Split Without a Leak: Reducing Privacy Leakage in Split Learningcited by 6
3 more
  1. 2023Learning in the Dark: Privacy-Preserving Machine Learning using Function Approximationcited by 6
  2. 2024Make Split, not Hijack: Preventing Feature-Space Hijacking Attacks in Split Learningcited by 6
  3. 2025Split Happens: Combating Advanced Threats with Split Learning and Function Secret Sharingcited by 1

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