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

arXiv: https://arxiv.org/abs/2408.05242 · HTML: https://mlsanity.com/paper/2408.05242/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 23 references that are in the map.
- First posted on arXiv 6 Aug 2024.

## Builds on (most cited first, 2 of 2)

- [SocialRec: User Activity Based Post Weighted Dynamic Personalized Post Recommendation System in Social Media](https://mlsanity.com/paper/2407.09747/) (2024)
- [EVOLVE: Predicting User Evolution and Network Dynamics in Social Media Using Fine-Tuned GPT-like Model](https://mlsanity.com/paper/2407.09691/) (2024)

## Built on by (most cited first, 1 of 1)

- [FedRobo: Federated Learning Driven Autonomous Inter Robots Communication For Optimal Chemical Sprays](https://mlsanity.com/paper/2408.06382/) (2024)

## Abstract (arXiv)

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.