Privacy-Preserving Data Deduplication for Enhancing Federated Learning of Language Models (Extended Version)
It addresses scalability and privacy issues in federated learning for language models, offering a practical solution for large-scale applications.
The paper tackles the problem of data deduplication in federated learning to improve model performance and efficiency while preserving privacy, achieving up to 19.62% improvement in perplexity and up to 27.95% reduction in running time.
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.