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FEAST: A Communication-efficient Federated Feature Selection Framework for Relational Data

Authors:

Meihui ZhangAuthors Info & Claims

Proceedings of the ACM on Management of Data, Volume 1, Issue 1

Article No.: 107, Pages 1 - 28

https://doi.org/10.1145/3588961

Published: 30 May 2023 Publication History

Abstract

Vertical federated learning (VFL) is an emerging paradigm for cross-silo organizations to build more accurate machine learning (ML) models. In this setting, multiple organizations (i.e., parties) hold the same set of samples with different features. However, different parties may have redundant or highly correlated features, leading to inefficient and ineffective VFL model training. Effective feature selection in VFL is therefore essential to mitigate such a problem and improve model effectiveness, as well as computation and communication efficiency. To this end, in this paper, we propose a federated feature selection framework, called FEAST, which leverages conditional mutual information (CMI) to select more informative features while having low redundancy. Furthermore, we design a communication-efficient method to reduce the information exchanged among the parties while protecting the parties' raw data. Extensive experiments on four real-world datasets demonstrate that the proposed framework achieves state-of-the-art performance in terms of accuracy, communication and computation costs.

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Zhu YWu YLuo ZOoi BXiao X(2024)Secure and Verifiable Data Collaboration with Low-Cost Zero-Knowledge ProofsProceedings of the VLDB Endowment10.14778/3665844.366586017:9(2321-2334)Online publication date: 6-Aug-2024
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Index Terms

FEAST: A Communication-efficient Federated Feature Selection Framework for Relational Data
1. Computing methodologies
  1. Artificial intelligence
    1. Distributed artificial intelligence
      1. Cooperation and coordination
  2. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning algorithms
      1. Feature selection
2. Mathematics of computing
  1. Information theory

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cover image Proceedings of the ACM on Management of Data

Proceedings of the ACM on Management of Data Volume 1, Issue 1

PACMMOD

May 2023

2807 pages

EISSN:2836-6573

DOI:10.1145/3603164

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Published: 30 May 2023

Published in PACMMOD Volume 1, Issue 1

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Chai DWang LYang LZhang JChen KYang Q(2024)A Survey for Federated Learning Evaluations: Goals and MeasuresIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.338200236:10(5007-5024)Online publication date: 27-Mar-2024
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