3 papers
cs.LG2026
ARES: Scalable and Practical Gradient Inversion Attack in Federated Learning through Activation Recovery
Zirui Gong, Leo Yu Zhang, Yanjun Zhang +4
Federated Learning (FL) enables collaborative model training by sharing model updates instead of raw data, aiming to protect user privacy. However, recent studies reveal that these…
cs.CV2025
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.LG2024
FLMarket: Enabling Privacy-preserved Pre-training Data Pricing for Federated Learning
Zhenyu Wen, Wanglei Feng, Di Wu +6
Federated Learning (FL), as a mainstream privacy-preserving machine learning paradigm, offers promising solutions for privacy-critical domains such as healthcare and finance. Altho…