Our paper introduces a novel approach to social network information retrieval and user engagement through a personalized chatbot system empowered by Federated Learning GPT. The system is designed to seamlessly aggregate and curate diverse social media data sources, including user posts, multimedia content, and trending news. Leveraging Federated Learning techniques, the GPT model is trained on decentralized data sources to ensure privacy and security while providing personalized insights and recommendations. Users interact with the chatbot through an intuitive interface, accessing tailored information and real-time updates on social media trends and user-generated content. The system's innovative architecture enables efficient processing of input files, parsing and enriching text data with metadata, and generating relevant questions and answers using advanced language models. By facilitating interactive access to a wealth of social network information, this personalized chatbot system represents a significant advancement in social media communication and knowledge dissemination.
@article{arxiv.2408.05242,
title = {FLASH: Federated Learning-Based LLMs for Advanced Query Processing in Social Networks through RAG},
author = {Sai Puppala and Ismail Hossain and Md Jahangir Alam and Sajedul Talukder},
journal= {arXiv preprint arXiv:2408.05242},
year = {2024}
}
Comments
This research paper is submitted to ASONAM 2024 conference on Advances in Social Networks Analysis and Mining and going to be published in Springer