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<p>In highly regulated domains such as finance and healthcare, where stringent data-sharing constraints pose substantial obstacles, federated learning (FL) has emerged as a transformative paradigm in distributed machine learning, facilitating collaborative model training, preserving data decentralization and upholding governance standards. Despite its advantages, FL is vulnerable to poisoning attacks during central model aggregation, prompting the development of Byzantine-robust FL systems that use robust aggregation rules to counter malicious attacks. However, neural network models in such systems are susceptible to unintentionally memorizing and revealing individual training instances, thereby introducing substantial information leakage risks, as adversaries may exploit this vulnerability to reconstruct sensitive data through model outputs transmitted over the air. Existing solutions fall short of providing a viable Byzantine-robust FL system that is completely secure against information leakage and is computationally efficient. To address these concerns, we propose Lancelot, an efficient and effective Byzantine-robust FL framework that uses fully homomorphic encryption to safeguard against malicious client activities. Lancelot introduces a mask-based encrypted sorting mechanism that overcomes the limitations of multiplication depth in ciphertext sorting with zero information leakage. It incorporates cryptographic enhancements like lazy relinearization, dynamic hoisting and GPU acceleration to ensure practical computational efficiency. Extensive experiments demonstrate that Lancelot surpasses existing approaches, achieving a 20-fold enhancement in processing speed.</p>

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Towards compute-efficient Byzantine-robust federated learning with fully homomorphic encryption

  • 期刊论文

  • 发表日期：

    2025年9月8日

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  • Siyang Jiang,
  • Hao Yang,
  • Qipeng Xie,
  • Chuan Ma,
  • Sen Wang,
  • Zhe Liu,
  • Tao Xiang,
  • Guoliang Xing

摘要

In highly regulated domains such as finance and healthcare, where stringent data-sharing constraints pose substantial obstacles, federated learning (FL) has emerged as a transformative paradigm in distributed machine learning, facilitating collaborative model training, preserving data decentralization and upholding governance standards. Despite its advantages, FL is vulnerable to poisoning attacks during central model aggregation, prompting the development of Byzantine-robust FL systems that use robust aggregation rules to counter malicious attacks. However, neural network models in such systems are susceptible to unintentionally memorizing and revealing individual training instances, thereby introducing substantial information leakage risks, as adversaries may exploit this vulnerability to reconstruct sensitive data through model outputs transmitted over the air. Existing solutions fall short of providing a viable Byzantine-robust FL system that is completely secure against information leakage and is computationally efficient. To address these concerns, we propose Lancelot, an efficient and effective Byzantine-robust FL framework that uses fully homomorphic encryption to safeguard against malicious client activities. Lancelot introduces a mask-based encrypted sorting mechanism that overcomes the limitations of multiplication depth in ciphertext sorting with zero information leakage. It incorporates cryptographic enhancements like lazy relinearization, dynamic hoisting and GPU acceleration to ensure practical computational efficiency. Extensive experiments demonstrate that Lancelot surpasses existing approaches, achieving a 20-fold enhancement in processing speed.