# SocFedGPT: Federated GPT-based Adaptive Content Filtering System Leveraging User Interactions in Social Networks

arXiv: https://arxiv.org/abs/2408.05243 · HTML: https://mlsanity.com/paper/2408.05243/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 16 references that are in the map.
- First posted on arXiv 7 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 study presents a multifaceted approach to enhancing user interaction and content relevance in social media platforms through a federated learning framework. We introduce personalized GPT and Context-based Social Media LLM models, utilizing federated learning for privacy and security. Four client entities receive a base GPT-2 model and locally collected social media data, with federated aggregation ensuring up-to-date model maintenance. Subsequent modules focus on categorizing user posts, computing user persona scores, and identifying relevant posts from friends' lists. A quantifying social engagement approach, coupled with matrix factorization techniques, facilitates personalized content suggestions in real-time. An adaptive feedback loop and readability score algorithm also enhance the quality and relevance of content presented to users. Our system offers a comprehensive solution to content filtering and recommendation, fostering a tailored and engaging social media experience while safeguarding user privacy.