research-article

No Free Lunch Theorem for Security and Utility in Federated Learning

Authors:

Qiang YangAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology, Volume 14, Issue 1

Article No.: 1, Pages 1 - 35

https://doi.org/10.1145/3563219

Published: 09 November 2022 Publication History

Abstract

In a federated learning scenario where multiple parties jointly learn a model from their respective data, there exist two conflicting goals for the choice of appropriate algorithms. On one hand, private and sensitive training data must be kept secure as much as possible in the presence of semi-honest partners; on the other hand, a certain amount of information has to be exchanged among different parties for the sake of learning utility. Such a challenge calls for the privacy-preserving federated learning solution, which maximizes the utility of the learned model and maintains a provable privacy guarantee of participating parties’ private data.

This article illustrates a general framework that (1) formulates the trade-off between privacy loss and utility loss from a unified information-theoretic point of view, and (2) delineates quantitative bounds of the privacy-utility trade-off when different protection mechanisms including randomization, sparsity, and homomorphic encryption are used. It was shown that in general there is no free lunch for the privacy-utility trade-off, and one has to trade the preserving of privacy with a certain degree of degraded utility. The quantitative analysis illustrated in this article may serve as the guidance for the design of practical federated learning algorithms.

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Taibi IRamon JPérez-González FComesaña-Alfaro PKrätzer CVicky Zhao H(2024)Honest Fraction Differential PrivacyProceedings of the 2024 ACM Workshop on Information Hiding and Multimedia Security10.1145/3658664.3659655(247-251)Online publication date: 24-Jun-2024
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Index Terms

No Free Lunch Theorem for Security and Utility in Federated Learning
1. Computer systems organization
  1. Dependable and fault-tolerant systems and networks
    1. Redundancy
  2. Embedded and cyber-physical systems
    1. Embedded systems
    2. Robotics
2. Networks
  1. Network properties
    1. Network reliability

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cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 14, Issue 1

February 2023

487 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/3570136

Editor:
Huan Liu
Arizona State University, USA

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Publication History

Published: 09 November 2022

Online AM: 20 September 2022

Accepted: 24 August 2022

Revised: 09 July 2022

Received: 10 March 2022

Published in TIST Volume 14, Issue 1

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Fu SDong FShen DChen RHao J(2024)DESIGN: Online Device Selection and Edge Association for Federated Synergy Learning-enabled AIoTACM Transactions on Intelligent Systems and Technology10.1145/3673237Online publication date: 15-Jun-2024
https://dl.acm.org/doi/10.1145/3673237
Taibi IRamon JPérez-González FComesaña-Alfaro PKrätzer CVicky Zhao H(2024)Honest Fraction Differential PrivacyProceedings of the 2024 ACM Workshop on Information Hiding and Multimedia Security10.1145/3658664.3659655(247-251)Online publication date: 24-Jun-2024
https://dl.acm.org/doi/10.1145/3658664.3659655
Zhang XFan LWang SLi WChen KYang Q(2024)A Game-theoretic Framework for Privacy-preserving Federated LearningACM Transactions on Intelligent Systems and Technology10.1145/365604915:3(1-35)Online publication date: 10-Apr-2024
https://dl.acm.org/doi/10.1145/3656049
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
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Luo JZhang YZhang JQin SWang HYu YXu ZEvans RShpitser I(2023)Practical privacy-preserving Gaussian process regression via secret sharingProceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence10.5555/3625834.3625958(1315-1325)Online publication date: 31-Jul-2023
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