SocFedGPT: Federated GPT-based Adaptive Content Filtering System Leveraging User Interactions in Social Networks
<|reference_start|>SocFedGPT: Federated GPT-based Adaptive Content Filtering System Leveraging User Interactions in Social Networks: 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.<|reference_end|>
@article{puppala2024socfedgpt:,
    title={SocFedGPT: Federated GPT-based Adaptive Content Filtering System
  Leveraging User Interactions in Social Networks},
    author={Sai Puppala, Ismail Hossain, Md Jahangir Alam, Sajedul Talukder},
    journal={arXiv preprint arXiv:2408.05243},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.05243},
    primaryClass={cs.LG cs.DC cs.IR cs.SI}
}