most citedTowards Dynamic Resource Allocation and Client Scheduling in Hierarchical Federated Learning: A Two-Phase Deep Reinforcement Learning Approach

1 citations · 1 across the 3 of their papers we have counts for

collaborators

3 papers

cs.CR2025

AEAS: Actionable Exploit Assessment System

Xiangmin Shen, Wenyuan Cheng, Yan Chen +6

Security practitioners face growing challenges in exploit assessment, as public vulnerability repositories are increasingly populated with inconsistent and low-quality exploit arti…

cs.CR2025

Towards Scalable and Interpretable Mobile App Risk Analysis via Large Language Models

Yu Yang, Zhenyuan Li, Xiandong Ran +4

Mobile application marketplaces are responsible for vetting apps to identify and mitigate security risks. Current vetting processes are labor-intensive, relying on manual analysis…

cs.LG2024★ 1 cited

Towards Dynamic Resource Allocation and Client Scheduling in Hierarchical Federated Learning: A Two-Phase Deep Reinforcement Learning Approach

Xiaojing Chen, Zhenyuan Li, Wei Ni +5

Federated learning (FL) is a viable technique to train a shared machine learning model without sharing data. Hierarchical FL (HFL) system has yet to be studied regrading its multip…