Identifying alternately poisoning attacks in federated learning online using trajectory anomaly detection method.

Implicit poisoning in federated learning is a significant threat, with malicious nodes subtly altering gradient parameters each round, making detection difficult. This study investigates this problem, revealing that temporal analysis alone struggles to identify such covert attacks, which can bypass online methods like cosine similarity and clustering. Common detection methods rely on offline analysis, resulting in delayed responses.

A Dual Attention Encoding Network Using Gradient Profile Loss for Oil Spill Detection Based on SAR Images.

Marine oil spills due to ship collisions or operational errors have caused tremendous damage to the marine environment. In order to better monitor the marine environment on a daily basis and reduce the damage and harm caused by oil pollution, we use marine image information acquired by synthetic aperture radar (SAR) and combine it with image segmentation techniques in deep learning to monitor oil spills. However, it is a significant challenge to accurately distinguish oil spill areas in original SAR images, which are characterized by high noise, blurred boundaries, and uneven intensity.