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Lookup NU author(s): Dr Aydin AbadiORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Deduplication is a vital preprocessing step that enhances machine learning model performance and saves training time and energy. However, enhancing federated learning through deduplication poses challenges, especially regarding scalability and potential privacy violations if deduplication involves sharingall clients’ data. In this paper, we address the problem of deduplication in a federated setup by introducing a pioneeringprotocol, Efficient Privacy-Preserving Multi-Party Deduplication (EP-MPD). It efficiently removes duplicates from multiple clients’ datasets without compromising data privacy. EP-MPD is con- structed in a modular fashion, utilizing two novel variants of the Private Set Intersection protocol. Our extensive experiments demonstrate the significant benefits of deduplication in federated learning of large language models. For instance, we observe up to 19.62% improvement in perplexity and up to 27.95% reduction in running time while varying the duplication level between 10% and 30%. EP-MPD effectively balances privacy and performance in federated learning, making it a valuable solution for large-scale applications.
Author(s): Abadi A, Dasu VA, Sarkar S
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: Distributed System Security Symposium (NDSS)
Year of Conference: 2025
Online publication date: 19/02/2025
Acceptance date: 07/02/2025
Date deposited: 08/01/2025
URL: https://www.ndss-symposium.org/ndss-paper/privacy-preserving-data-deduplication-for-enhancing-federated-learning-of-language-models/
ePrints DOI: 10.57711/w6t1-c442
