Camel: Communication-Efficient and Maliciously Secure Federated Learning in the Shuffle Model of Differential Privacy

• URL: http://arxiv.org/abs/2410.03407v1
• Date: Fri, 4 Oct 2024 13:13:44 GMT
• Title: Camel: Communication-Efficient and Maliciously Secure Federated Learning in the Shuffle Model of Differential Privacy
• Authors: Shuangqing Xu, Yifeng Zheng, Zhongyun Hua,
• Abstract summary: Federated learning (FL) has rapidly become a compelling paradigm that enables multiple clients to jointly train a model by sharing only gradient updates for aggregation. In order to protect the gradient updates which could also be privacy-sensitive, there has been a line of work studying local differential privacy mechanisms. We present Camel, a new communication-efficient and maliciously secure FL framework in the shuffle model of DP.
• Score: 9.100955087185811
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated learning (FL) has rapidly become a compelling paradigm that enables multiple clients to jointly train a model by sharing only gradient updates for aggregation, without revealing their local private data. In order to protect the gradient updates which could also be privacy-sensitive, there has been a line of work studying local differential privacy (LDP) mechanisms to provide a formal privacy guarantee. With LDP mechanisms, clients locally perturb their gradient updates before sharing them out for aggregation. However, such approaches are known for greatly degrading the model utility, due to heavy noise addition. To enable a better privacy-utility tradeoff, a recently emerging trend is to apply the shuffle model of DP in FL, which relies on an intermediate shuffling operation on the perturbed gradient updates to achieve privacy amplification. Following this trend, in this paper, we present Camel, a new communication-efficient and maliciously secure FL framework in the shuffle model of DP. Camel first departs from existing works by ambitiously supporting integrity check for the shuffle computation, achieving security against malicious adversary. Specifically, Camel builds on the trending cryptographic primitive of secret-shared shuffle, with custom techniques we develop for optimizing system-wide communication efficiency, and for lightweight integrity checks to harden the security of server-side computation. In addition, we also derive a significantly tighter bound on the privacy loss through analyzing the Renyi differential privacy (RDP) of the overall FL process. Extensive experiments demonstrate that Camel achieves better privacy-utility trade-offs than the state-of-the-art work, with promising performance.

Related papers

• DMM: Distributed Matrix Mechanism for Differentially-Private Federated Learning using Packed Secret Sharing [51.336015600778396]
  Federated Learning (FL) has gained lots of traction recently, both in industry and academia. In FL, a machine learning model is trained using data from various end-users arranged in committees across several rounds. Since such data can often be sensitive, a primary challenge in FL is providing privacy while still retaining utility of the model.
  arXiv  Detail & Related papers  (2024-10-21T16:25:14Z)
• PriRoAgg: Achieving Robust Model Aggregation with Minimum Privacy Leakage for Federated Learning [49.916365792036636]
  Federated learning (FL) has recently gained significant momentum due to its potential to leverage large-scale distributed user data. The transmitted model updates can potentially leak sensitive user information, and the lack of central control of the local training process leaves the global model susceptible to malicious manipulations on model updates. We develop a general framework PriRoAgg, utilizing Lagrange coded computing and distributed zero-knowledge proof, to execute a wide range of robust aggregation algorithms while satisfying aggregated privacy.
  arXiv  Detail & Related papers  (2024-07-12T03:18:08Z)
• Noise-Aware Algorithm for Heterogeneous Differentially Private Federated Learning [21.27813247914949]
  We propose Robust-HDP, which efficiently estimates the true noise level in clients model updates. It improves utility and convergence speed, while being safe to the clients that may maliciously send falsified privacy parameter to server.
  arXiv  Detail & Related papers  (2024-06-05T17:41:42Z)
• Tighter Privacy Auditing of DP-SGD in the Hidden State Threat Model [40.4617658114104]
  In this work, we focus on a threat model where the adversary has access only to the final model, with no visibility into intermediate updates. Our experiments show that this approach consistently outperforms previous attempts at auditing the hidden state model. Our results advance the understanding of achievable privacy guarantees within this threat model.
  arXiv  Detail & Related papers  (2024-05-23T11:38:38Z)
• Binary Federated Learning with Client-Level Differential Privacy [7.854806519515342]
  Federated learning (FL) is a privacy-preserving collaborative learning framework. Existing FL systems typically adopt Federated Average (FedAvg) as the training algorithm. We propose a communication-efficient FL training algorithm with differential privacy guarantee.
  arXiv  Detail & Related papers  (2023-08-07T06:07:04Z)
• Balancing Privacy and Performance for Private Federated Learning Algorithms [4.681076651230371]
  Federated learning (FL) is a distributed machine learning framework where multiple clients collaborate to train a model without exposing their private data. FL algorithms frequently employ a differential privacy mechanism that introduces noise into each client's model updates before sharing. We show that an optimal balance exists between the number of local steps and communication rounds, one that maximizes the convergence performance within a given privacy budget.
  arXiv  Detail & Related papers  (2023-04-11T10:42:11Z)
• FedLAP-DP: Federated Learning by Sharing Differentially Private Loss Approximations [53.268801169075836]
  We propose FedLAP-DP, a novel privacy-preserving approach for federated learning. A formal privacy analysis demonstrates that FedLAP-DP incurs the same privacy costs as typical gradient-sharing schemes. Our approach presents a faster convergence speed compared to typical gradient-sharing methods.
  arXiv  Detail & Related papers  (2023-02-02T12:56:46Z)
• Over-the-Air Federated Learning with Privacy Protection via Correlated Additive Perturbations [57.20885629270732]
  We consider privacy aspects of wireless federated learning with Over-the-Air (OtA) transmission of gradient updates from multiple users/agents to an edge server. Traditional perturbation-based methods provide privacy protection while sacrificing the training accuracy. In this work, we aim at minimizing privacy leakage to the adversary and the degradation of model accuracy at the edge server.
  arXiv  Detail & Related papers  (2022-10-05T13:13:35Z)
• PRECAD: Privacy-Preserving and Robust Federated Learning via Crypto-Aided Differential Privacy [14.678119872268198]
  Federated Learning (FL) allows multiple participating clients to train machine learning models collaboratively by keeping their datasets local and only exchanging model updates. Existing FL protocol designs have been shown to be vulnerable to attacks that aim to compromise data privacy and/or model robustness. We develop a framework called PRECAD, which simultaneously achieves differential privacy (DP) and enhances robustness against model poisoning attacks with the help of cryptography.
  arXiv  Detail & Related papers  (2021-10-22T04:08:42Z)
• Understanding Clipping for Federated Learning: Convergence and Client-Level Differential Privacy [67.4471689755097]
  This paper empirically demonstrates that the clipped FedAvg can perform surprisingly well even with substantial data heterogeneity. We provide the convergence analysis of a differential private (DP) FedAvg algorithm and highlight the relationship between clipping bias and the distribution of the clients' updates.
  arXiv  Detail & Related papers  (2021-06-25T14:47:19Z)
• Differentially Private Federated Learning with Laplacian Smoothing [72.85272874099644]
  Federated learning aims to protect data privacy by collaboratively learning a model without sharing private data among users. An adversary may still be able to infer the private training data by attacking the released model. Differential privacy provides a statistical protection against such attacks at the price of significantly degrading the accuracy or utility of the trained models.
  arXiv  Detail & Related papers  (2020-05-01T04:28:38Z)

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.