Improving Privacy-Preserving Vertical Federated Learning by Efficient Communication with ADMM

Chulin Xie, Pin Yu Chen, Qinbin Li, Arash Nourian, Ce Zhang, Bo Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Federated learning (FL) enables distributed resource- constrained devices to jointly train shared models while keeping the training data local for privacy purposes. Vertical FL (VFL), which allows each client to collect partial features, has attracted intensive research efforts recently. We identified the main challenges that existing VFL frameworks are facing: the server needs to communicate gradients with the clients for each training step, incurring high communication cost that leads to rapid consumption of privacy budgets. To address these challenges, in this paper, we introduce a VFL framework with multiple heads (VIM ), which takes the separate contribution of each client into account, and enables an efficient decomposition of the VFL optimization objective to sub-objectives that can be iteratively tackled by the server and the clients on their own. In particular, we propose an Alternating Direction Method of Multipliers (ADMM)- based method to solve our optimization problem, which allows clients to conduct multiple local updates before communication, and thus reduces the communication cost and leads to better performance under differential privacy (DP). We provide the client-level DP mechanism for our framework to protect user privacy. Moreover, we show that a byproduct of VIM is that the weights of learned heads reflect the importance of local clients. We conduct extensive evaluations and show that on four vertical FL datasets, VIM achieves significantly higher performance and faster convergence compared with the state-of-the-art. We also explicitly evaluate the importance of local clients and show that VIM enables functionalities such as client-level explanation and client denoising. We hope this work will shed light on a new way of effective VFL training and understanding.

Original languageEnglish (US)
Title of host publicationProceedings - IEEE Conference on Safe and Trustworthy Machine Learning, SaTML 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages443-471
Number of pages29
ISBN (Electronic)9798350349504
DOIs
StatePublished - 2024
Externally publishedYes
Event2024 IEEE Conference on Safe and Trustworthy Machine Learning, SaTML 2024 - Toronto, Canada
Duration: Apr 9 2024Apr 11 2024

Publication series

NameProceedings - IEEE Conference on Safe and Trustworthy Machine Learning, SaTML 2024

Conference

Conference2024 IEEE Conference on Safe and Trustworthy Machine Learning, SaTML 2024
Country/TerritoryCanada
CityToronto
Period4/9/244/11/24

Keywords

  • ADMM
  • Communication-Efficiency
  • Differential Privacy
  • Vertical Federated Learning

ASJC Scopus subject areas

  • Artificial Intelligence
  • Safety, Risk, Reliability and Quality
  • Modeling and Simulation

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