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

TL;DR

SocFedGPT introduces a federated learning-based system utilizing personalized GPT models and social engagement metrics to improve content filtering and recommendations in social media, ensuring privacy and relevance.

Contribution

The paper presents a novel federated GPT framework with personalized models and social engagement techniques for adaptive content filtering in social networks.

Findings

Effective personalized content recommendations achieved.

Enhanced privacy through federated learning.

Improved user engagement and content relevance.

Abstract

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.