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Federated Learning for Empowering Large Language Models

EasyChair Preprint no. 10828

6 pagesDate: September 4, 2023


The rapid evolution of large language models has transformed various natural language processing tasks, but their centralized training necessitates extensive data sharing, raising privacy and security concerns. Federated Learning (FL) presents a promising paradigm to address these challenges by training models collaboratively across decentralized devices while preserving data privacy. This paper delves into the application of Federated Learning to empower large language models. We explore the theoretical foundations of FL in the context of language model training and investigate its practical implementation challenges. By distributing the training process, FL enables the development of large language models without requiring raw data to leave user devices, thereby enhancing privacy and reducing communication overhead. We analyze various FL strategies tailored to language model training, encompassing aggregation methods, communication protocols, and optimization techniques. Additionally, we discuss the trade-offs between FL and conventional centralized training approaches, considering factors such as convergence speed, model performance, and resource consumption. Furthermore, real-world use cases of FL for language models are examined, highlighting its potential impact across applications like personalized AI assistants, language translation, and sentiment analysis. Through this comprehensive exploration, we emphasize the transformable potential of Federated Learning in advancing the capabilities of large language models while preserving data privacy and security.

Keyphrases: Aggregation methods, collaboration, communication protocols, convergence speed, data privacy, Decentralized Devices, decentralized training, distributed learning, Federated Learning, Federated NLP, Language Translation, large language models, model performance, Optimization Techniques, Personalized AI Assistants, privacy enhancement, privacy-preserving learning, resource efficiency, Security, Sentiment Analysis

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Soumik Deb Niloy and Abdullah Jamil Sifat and Md Asaduzzaman Sarker Anik and Mohammad Jawad Ul Tashick and Mashira Rofi},
  title = {Federated Learning for Empowering Large Language Models},
  howpublished = {EasyChair Preprint no. 10828},

  year = {EasyChair, 2023}}
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