Privacy-Preserving Data Deduplication for Enhancing Federated Learning of Language Models
Aydin Abadi, Vishnu Asutosh Dasu, Sumanta Sarkar · 2025
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 sharing all clients' data.In this paper, we address the problem of deduplication in a federated setup by introducing a pioneering protocol, Efficient Privacy-Preserving Multi-Party Deduplication (EP-MPD).It efficiently removes duplicates from multiple clients' datasets without compromising data privacy.EP-MPD is constructed 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.* Equal contribution.Listing order is alphabetical.n i=1 S i ) we mean the concatenation of the sets which may result in a multi-set.We denote an empty set by ∅.We denote a size of vector ⃗ v with |⃗ v|.We assume that the server and all the users have access to secure channels among them.By the notation, X c ≡ Y, we mean that the two distributions X and Y are computationally indistinguishable.