FLASH: Federated Learning-Based LLMs for Advanced Query Processing in Social Networks through RAG

TL;DR

This paper presents a federated learning-based personalized chatbot system for social networks that aggregates diverse social media data, ensuring privacy while providing tailored insights and real-time updates.

Contribution

It introduces a novel federated learning approach for training GPT models on decentralized social media data for enhanced user engagement.

Findings

Effective aggregation of diverse social media data sources

Privacy-preserving federated training of GPT models

Improved user engagement through personalized insights

Abstract

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.