An Exploratory Analysis on Users' Contributions in Federated Learning

Jiyue Huang, Rania Talbi, Zilong Zhao, Sara Boucchenak, Lydia Y. Chen, Stefanie Roos

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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Abstract

Federated Learning is an emerging distributed collaborative learning paradigm adopted by many of today's applications, e.g., keyboard prediction and object recognition. Its core principle is to learn from large amount of users data while preserving data privacy by design as collaborative users only need to share the machine learning models and keep data locally. The main challenge for such systems is to provide incentives to users to contribute high-quality models trained from their local data. In this paper, we aim to answer how well incentives recognize (in)accurate local models from honest and malicious users, and perceive their impacts on the model accuracy of federated learning systems. We first present a thorough survey on two contrasting perspectives: incentive mechanisms to measure the contribution of local models by honest users, and malicious users to deliberately degrade the overall model. We conduct simulation experiments to empirically demonstrate if existing contribution measurement schemes can disclose low-quality models from malicious users. Our results show there exists a clear tradeoff among measurement schemes in terms of the computational efficiency and effectiveness to distill the impact of malicious participants. We conclude this paper by discussing the research directions to design resilient contribution incentives.

Original languageEnglish
Title of host publicationProceedings - 2020 2nd IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications, TPS-ISA 2020
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages20-29
Number of pages10
ISBN (Electronic)9781728185439
DOIs
Publication statusPublished - 2020
Event2nd IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications, TPS-ISA 2020 - Virtual, Atlanta, United States
Duration: 1 Dec 20203 Dec 2020

Publication series

NameProceedings - 2020 2nd IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications, TPS-ISA 2020

Conference

Conference2nd IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications, TPS-ISA 2020
CountryUnited States
CityVirtual, Atlanta
Period1/12/203/12/20

Keywords

  • Adversarial Behavior
  • Contribution Measurement
  • Federated Learning
  • Incentive Mechanisms

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