REVIEW 1 cited by
FLASH: Federated Learning-Based LLMs for Advanced Query Processing in Social Networks through RAG
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
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.
Forward citations
Cited by 1 Pith paper
-
One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning
PRoBit+ combines one-bit stochastic quantization, ML-based aggregation, and an adaptive quantization range to achieve communication-efficient, differentially private, Byzantine-robust personalized federated learning.
Discussion (0). Sign in to comment.