5 papers
QoS-Aware Token Scheduling and Private Data Valuation for Multi-Modal Agentic Networks
Yao Du, Jing Liu, Pengfei Xu +4
In agentic systems, human-generated data records anchor the value of AI services. Yet cloud compute pipelines centralize processing on remote servers. Data centralization reduces p…
FedRE: A Representation Entanglement Framework for Model-Heterogeneous Federated Learning
Yuan Yao, Lixu Wang, Jiaqi Wu +7
Federated learning (FL) enables collaborative training across clients while preserving privacy. While most existing FL methods assume homogeneous model architectures, client hetero…
Multi-Agent Honeypot-Based Request-Response Context Dataset for Improved SQL Injection Detection Performance
Hao Yu, Hui Li, FengYuan Shi +4
SQL injection remains a major threat to web applications, as existing defenses often fail against obfuscation and evolving attacks because of neglecting the request-response contex…
A Closer Look at Personalized Fine-Tuning in Heterogeneous Federated Learning
Minghui Chen, Hrad Ghoukasian, Ruinan Jin +3
Federated Learning (FL) enables decentralized, privacy-preserving model training but struggles to balance global generalization and local personalization due to non-identical data…
Clio-X: AWeb3 Solution for Privacy-Preserving AI Access to Digital Archives
Victoria L. Lemieux, Rosa Gil, Faith Molosiwa +5
As archives turn to artificial intelligence to manage growing volumes of digital records, privacy risks inherent in current AI data practices raise critical concerns about data sov…