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arXiv · 2408.05243

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

Sai Puppala·Ismail Hossain·Md Jahangir Alam·Sajedul Talukder
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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.

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Sai Puppala, Ismail Hossain, Md Jahangir Alam, Sajedul Talukder. 2024-08-07. SocFedGPT: Federated GPT-based Adaptive Content Filtering System Leveraging User Interactions in Social Networks. https://arxiv.org/abs/2408.05243

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