By Swati Agarwal, BITS Pilani, Goa

Abstract 

In the realm of Natural Language Processing (NLP), the imperative to leverage vast and diverse datasets has never been more pronounced. However, concerns regarding data privacy, security, and regulatory compliance pose formidable challenges to conventional centralized approaches. Federated Learning emerges as a paradigm-shifting solution that enables model training on decentralized data sources while preserving individual data privacy. This seminar aims to provide a comprehensive exploration of Federated Learning in the context of NLP applications. We will delve into the fundamental principles, methodologies, and challenges associated with this cutting-edge technique. By distributing model training across multiple devices or servers, Federated Learning not only ensures data privacy but also promotes collaboration among institutions and organizations with shared research interests. This presentation will also serve as a foundation for further discussions and research endeavors in the domain of Federated Learning and NLP, creating an opportunity for collaboration and knowledge exchange among esteemed faculty members and experts.