Date of Publication :25th July 2024

Abstract:Large language models (LLMs) have revolutionized natural language processing, pushing the boundaries of what machines can understand and generate text. These complex models, trained on massive datasets, excel at various tasks like summarizing factual topics, creating all kinds of creative content, and translating languages. However, their strength lies in their generality, and to truly shine on specific tasks, they require fine- tuning. This fine-tuning process tailors the LLM to a particular domain or application, significantly boosting its performance. Traditionally, this fine-tuning relies on centralized data stor- age, where vast amounts of user data are aggregated in one location. This approach raises significant challenges regarding data privacy and security. Users often hesitate to share their data, and regulations regarding data ownership and transfer can create roadblocks. Federated learning offers a promising solution to these challenges. It enables collaborative training on decentralized datasets stored on individual devices or local servers. This approach protects user privacy by keeping the data local while allowing the LLM to leverage the collective knowledge from these distributed sources. By exploring federated learning for fine-tuning LLMs, this research aims to bridge the gap between generic capabilities and domain-specific expertise, boosting the development of efficient and privacy-preserving language models.

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