3 papers
cs.CV2024
Can't Slow me Down: Learning Robust and Hardware-Adaptive Object Detectors against Latency Attacks for Edge Devices
Tianyi Wang, Zichen Wang, Cong Wang +4
Object detection is a fundamental enabler for many real-time downstream applications such as autonomous driving, augmented reality and supply chain management. However, the algorit…
cs.CR2023
Vulnerability of Machine Learning Approaches Applied in IoT-based Smart Grid: A Review
Zhenyong Zhang, Mengxiang Liu, Mingyang Sun +5
Machine learning (ML) sees an increasing prevalence of being used in the internet-of-things (IoT)-based smart grid. However, the trustworthiness of ML is a severe issue that must b…
cs.LG2022
FedSiam-DA: Dual-aggregated Federated Learning via Siamese Network under Non-IID Data
Ming Yang, Yanhan Wang, Xin Wang +3
Federated learning is a distributed learning that allows each client to keep the original data locally and only upload the parameters of the local model to the server. Despite fede…