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Youliang Tian

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#federated learning Open access Sep 2026

RetFL: a privacy-preserving and traceable framework for robust federated learning

Federated learning (FL) enables multiple clients to jointly train a model without sharing raw data. Decentralized federated learning (DFL) further removes the need for a trusted central coordinator in the aggregation process. However, in decentralized settings, model aggregation is vulnerable to inference and poisoning...

Yi-Cheng Huang, Zhou Zhou, You-Liang Tian et al. · 0 citations
#edge computing Sep 2026

Toward 6G Edge Intelligence: Lightweight LLMs for Intent-Driven Network Automation

Future 6G networks are envisaged to tightly integrate communication, sensing, and computing, demanding real-time, intent-driven intelligence at the edge. While large language models (LLMs) excel in intent recognition and semantic reasoning, their application to real-time network lifecycle management at the edge is limi...

Bing Wu, Sai Zou, Minghui Liwang et al. · 4 citations

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