4 citations · 5 across the 11 of their papers we have counts for
4 papers · 1 filter
Byzantine-Resilient Federated Large Language Model via Hypergraph Message Passing
Kai Li, Hanlin Cai, Zihao Liu +5
Federated Large Language Models (FedLLMs) are promising to deliver privacy-preserving, collaborative intelligence. The decentralized training process is vulnerable to Byzantine att…
FedLNS: Leverage LayerNorm Signature Modeling to Mitigate Adversarial Manipulation in Federated LLMs
Kai Li, Jong-Ik Park, Carlee Joe-Wong +2
Federated training enables language models to learn from distributed private text, but the server cannot directly verify the local supervision or optimization process that produces…
Undermining Federated Learning Accuracy in EdgeIoT via Variational Graph Auto-Encoders
Kai Li, Shuyan Hu, Bochun Wu +3
EdgeIoT represents an approach that brings together mobile edge computing with Internet of Things (IoT) devices, allowing for data processing close to the data source. Sending sour…
Human-In-The-Loop Machine Learning for Safe and Ethical Autonomous Vehicles: Principles, Challenges, and Opportunities
Yousef Emami, Mohammadhossein Homaei, Miguel Gutiérrez Gaitán +4
Machine Learning (ML) has become central to Autonomous Vehicles (AVs), supporting perception, prediction, planning, control, and decision-making in dynamic environments. However, a…