research-article

PEFL: A Privacy-Enhanced Federated Learning Scheme for Big Data Analytics

Authors:

Hai DengAuthors Info & Claims

2019 IEEE Global Communications Conference (GLOBECOM)

Pages 1 - 6

https://doi.org/10.1109/GLOBECOM38437.2019.9014272

Published: 01 December 2019 Publication History

Abstract

Federated learning has emerged as a promising solution for big data analytics, which jointly trains a global model across multiple mobile devices. However, participants' sensitive data information may be leaked to an untrusted server through uploaded gradient vectors. To address this problem, we propose a privacy-enhanced federated learning (PEFL) scheme to protect the gradients over an untrusted server. This is mainly enabled by encrypting participants' local gradients with Paillier homomorphic cryptosystem. In order to reduce the computation costs of the cryptosystem, we utilize the distributed selective stochastic gradient descent (DSSGD) method in the local training phase to achieve the distributed encryption. Moreover, the encrypted gradients can be further used for secure sum aggregation at the server side. In this way, the untrusted server can only learn the aggregated statistics for all the participants' updates, while each individual's private information will be well-protected. For the security analysis, we theoretically prove that our scheme is secure under several cryptographic hard problems. Exhaustive experimental results demonstrate that PEFL has low computation costs while reaching high accuracy in the settings of federated learning.

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Cited By

Zhang XKang YFan LChen KYang Q(2024)A Meta-Learning Framework for Tuning Parameters of Protection Mechanisms in Trustworthy Federated LearningACM Transactions on Intelligent Systems and Technology10.1145/365261215:3(1-36)Online publication date: 18-Mar-2024
https://dl.acm.org/doi/10.1145/3652612
Alebouyeh ZBidgoly A(2024)Benchmarking robustness and privacy-preserving methods in federated learningFuture Generation Computer Systems10.1016/j.future.2024.01.009155:C(18-38)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1016/j.future.2024.01.009
Chen HZhu TZhang TZhou WYu P(2023)Privacy and Fairness in Federated Learning: On the Perspective of TradeoffACM Computing Surveys10.1145/360601756:2(1-37)Online publication date: 15-Sep-2023
https://dl.acm.org/doi/10.1145/3606017
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PEFL: A Privacy-Enhanced Federated Learning Scheme for Big Data Analytics

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2019 IEEE Global Communications Conference (GLOBECOM)

6544 pages

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Published: 01 December 2019

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Cited By

Zhang XKang YFan LChen KYang Q(2024)A Meta-Learning Framework for Tuning Parameters of Protection Mechanisms in Trustworthy Federated LearningACM Transactions on Intelligent Systems and Technology10.1145/365261215:3(1-36)Online publication date: 18-Mar-2024
https://dl.acm.org/doi/10.1145/3652612
Alebouyeh ZBidgoly A(2024)Benchmarking robustness and privacy-preserving methods in federated learningFuture Generation Computer Systems10.1016/j.future.2024.01.009155:C(18-38)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1016/j.future.2024.01.009
Chen HZhu TZhang TZhou WYu P(2023)Privacy and Fairness in Federated Learning: On the Perspective of TradeoffACM Computing Surveys10.1145/360601756:2(1-37)Online publication date: 15-Sep-2023
https://dl.acm.org/doi/10.1145/3606017
Zhang XKang YChen KFan LYang Q(2023)Trading Off Privacy, Utility, and Efficiency in Federated LearningACM Transactions on Intelligent Systems and Technology10.1145/359518514:6(1-32)Online publication date: 5-May-2023
https://dl.acm.org/doi/10.1145/3595185
Zhu JCao JSaxena DJiang SFerradi H(2023)Blockchain-empowered Federated Learning: Challenges, Solutions, and Future DirectionsACM Computing Surveys10.1145/357095355:11(1-31)Online publication date: 22-Feb-2023
https://dl.acm.org/doi/10.1145/3570953
Issa WMoustafa NTurnbull BSohrabi NTari Z(2023)Blockchain-Based Federated Learning for Securing Internet of Things: A Comprehensive SurveyACM Computing Surveys10.1145/356081655:9(1-43)Online publication date: 16-Jan-2023
https://dl.acm.org/doi/10.1145/3560816
Zhang XGu HFan LChen KYang Q(2022)No Free Lunch Theorem for Security and Utility in Federated LearningACM Transactions on Intelligent Systems and Technology10.1145/356321914:1(1-35)Online publication date: 9-Nov-2022
https://dl.acm.org/doi/10.1145/3563219

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