Lancelot: Towards Efficient and Privacy-Preserving Byzantine-Robust Federated Learning within Fully Homomorphic Encryption

• URL: http://arxiv.org/abs/2408.06197v1
• Date: Mon, 12 Aug 2024 14:48:25 GMT
• Title: Lancelot: Towards Efficient and Privacy-Preserving Byzantine-Robust Federated Learning within Fully Homomorphic Encryption
• Authors: Siyang Jiang, Hao Yang, Qipeng Xie, Chuan Ma, Sen Wang, Guoliang Xing,
• Abstract summary: We propose Lancelot, an innovative and computationally efficient BRFL framework that employs fully homomorphic encryption (FHE) to safeguard against malicious client activities while preserving data privacy. Our extensive testing, which includes medical imaging diagnostics and widely-used public image datasets, demonstrates that Lancelot significantly outperforms existing methods, offering more than a twenty-fold increase in processing speed, all while maintaining data privacy.
• Score: 10.685816010576918
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In sectors such as finance and healthcare, where data governance is subject to rigorous regulatory requirements, the exchange and utilization of data are particularly challenging. Federated Learning (FL) has risen as a pioneering distributed machine learning paradigm that enables collaborative model training across multiple institutions while maintaining data decentralization. Despite its advantages, FL is vulnerable to adversarial threats, particularly poisoning attacks during model aggregation, a process typically managed by a central server. However, in these systems, neural network models still possess the capacity to inadvertently memorize and potentially expose individual training instances. This presents a significant privacy risk, as attackers could reconstruct private data by leveraging the information contained in the model itself. Existing solutions fall short of providing a viable, privacy-preserving BRFL system that is both completely secure against information leakage and computationally efficient. To address these concerns, we propose Lancelot, an innovative and computationally efficient BRFL framework that employs fully homomorphic encryption (FHE) to safeguard against malicious client activities while preserving data privacy. Our extensive testing, which includes medical imaging diagnostics and widely-used public image datasets, demonstrates that Lancelot significantly outperforms existing methods, offering more than a twenty-fold increase in processing speed, all while maintaining data privacy.

Related papers

• EPIC: Enhancing Privacy through Iterative Collaboration [4.199844472131922]
  Traditional machine learning techniques require centralized data collection and processing. Privacy, ownership, and stringent regulation issues exist when pooling medical data into centralized storage. The Federated learning (FL) approach overcomes such issues by setting up a central aggregator server and a shared global model.
  arXiv  Detail & Related papers  (2024-11-07T20:10:34Z)
• Pseudo-Probability Unlearning: Towards Efficient and Privacy-Preserving Machine Unlearning [59.29849532966454]
  We propose PseudoProbability Unlearning (PPU), a novel method that enables models to forget data to adhere to privacy-preserving manner. Our method achieves over 20% improvements in forgetting error compared to the state-of-the-art.
  arXiv  Detail & Related papers  (2024-11-04T21:27:06Z)
• EncCluster: Scalable Functional Encryption in Federated Learning through Weight Clustering and Probabilistic Filters [3.9660142560142067]
  Federated Learning (FL) enables model training across decentralized devices by communicating solely local model updates to an aggregation server. FL remains vulnerable to inference attacks during model update transmissions. We present EncCluster, a novel method that integrates model compression through weight clustering with recent decentralized FE and privacy-enhancing data encoding.
  arXiv  Detail & Related papers  (2024-06-13T14:16:50Z)
• Blockchain-empowered Federated Learning for Healthcare Metaverses: User-centric Incentive Mechanism with Optimal Data Freshness [66.3982155172418]
  We first design a user-centric privacy-preserving framework based on decentralized Federated Learning (FL) for healthcare metaverses. We then utilize Age of Information (AoI) as an effective data-freshness metric and propose an AoI-based contract theory model under Prospect Theory (PT) to motivate sensing data sharing.
  arXiv  Detail & Related papers  (2023-07-29T12:54:03Z)
• PS-FedGAN: An Efficient Federated Learning Framework Based on Partially Shared Generative Adversarial Networks For Data Privacy [56.347786940414935]
  Federated Learning (FL) has emerged as an effective learning paradigm for distributed computation. This work proposes a novel FL framework that requires only partial GAN model sharing. Named as PS-FedGAN, this new framework enhances the GAN releasing and training mechanism to address heterogeneous data distributions.
  arXiv  Detail & Related papers  (2023-05-19T05:39:40Z)
• Federated Learning with Privacy-Preserving Ensemble Attention Distillation [63.39442596910485]
  Federated Learning (FL) is a machine learning paradigm where many local nodes collaboratively train a central model while keeping the training data decentralized. We propose a privacy-preserving FL framework leveraging unlabeled public data for one-way offline knowledge distillation. Our technique uses decentralized and heterogeneous local data like existing FL approaches, but more importantly, it significantly reduces the risk of privacy leakage.
  arXiv  Detail & Related papers  (2022-10-16T06:44:46Z)
• Distributed Machine Learning and the Semblance of Trust [66.1227776348216]
  Federated Learning (FL) allows the data owner to maintain data governance and perform model training locally without having to share their data. FL and related techniques are often described as privacy-preserving. We explain why this term is not appropriate and outline the risks associated with over-reliance on protocols that were not designed with formal definitions of privacy in mind.
  arXiv  Detail & Related papers  (2021-12-21T08:44:05Z)
• A Privacy-Preserving and Trustable Multi-agent Learning Framework [34.28936739262812]
  This paper presents Privacy-preserving and trustable Distributed Learning (PT-DL) PT-DL is a fully decentralized framework that relies on Differential Privacy to guarantee strong privacy protections of the agents' data. The paper shows that PT-DL is resilient up to a 50% collusion attack, with high probability, in a malicious trust model.
  arXiv  Detail & Related papers  (2021-06-02T15:46:27Z)
• Federated Learning in Adversarial Settings [0.8701566919381224]
  Federated learning scheme provides different trade-offs between robustness, privacy, bandwidth efficiency, and model accuracy. We show that this extension performs as efficiently as the non-private but robust scheme, even with stringent privacy requirements. This suggests a possible fundamental trade-off between Differential Privacy and robustness.
  arXiv  Detail & Related papers  (2020-10-15T14:57:02Z)
• SPEED: Secure, PrivatE, and Efficient Deep learning [2.283665431721732]
  We introduce a deep learning framework able to deal with strong privacy constraints. Based on collaborative learning, differential privacy and homomorphic encryption, the proposed approach advances state-of-the-art.
  arXiv  Detail & Related papers  (2020-06-16T19:31:52Z)
• Privacy-preserving Traffic Flow Prediction: A Federated Learning Approach [61.64006416975458]
  We propose a privacy-preserving machine learning technique named Federated Learning-based Gated Recurrent Unit neural network algorithm (FedGRU) for traffic flow prediction. FedGRU differs from current centralized learning methods and updates universal learning models through a secure parameter aggregation mechanism. It is shown that FedGRU's prediction accuracy is 90.96% higher than the advanced deep learning models.
  arXiv  Detail & Related papers  (2020-03-19T13:07:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.