5 papers
Towards Explainable Privacy Preservation in Federated Learning via Shapley Value-Guided Noise Injection
Yunbo Li, Jiaping Gui, Yue Wu
This paper proposes FedSVA, an explainable differential privacy (DP) mechanism for federated learning (FL) that dynamically calibrates noise injection based on the privacy contribu…
Local Layer-wise Differential Privacy in Federated Learning
Yunbo Li, Jiaping Gui, Fanchao Meng +1
Federated Learning (FL) enables collaborative model training without direct data sharing, yet it remains vulnerable to privacy attacks such as model inversion and membership infere…
FedQS: Optimizing Gradient and Model Aggregation for Semi-Asynchronous Federated Learning
Yunbo Li, Jiaping Gui, Zhihang Deng +2
Federated learning (FL) enables collaborative model training across multiple parties without sharing raw data, with semi-asynchronous FL (SAFL) emerging as a balanced approach betw…
RHINO: Guided Reasoning for Mapping Network Logs to Adversarial Tactics and Techniques with Large Language Models
Fanchao Meng, Jiaping Gui, Yunbo Li +1
Modern Network Intrusion Detection Systems generate vast volumes of low-level alerts, yet these outputs remain semantically fragmented, requiring labor-intensive manual correlation…
An Experimental Study of Different Aggregation Schemes in Semi-Asynchronous Federated Learning
Yunbo Li, Jiaping Gui, Yue Wu
Federated learning is highly valued due to its high-performance computing in distributed environments while safeguarding data privacy. To address resource heterogeneity, researcher…