With the continuous development of deep learning in the field of image generation models, a
large number of vivid forged faces have been generated and spread on the Internet. These …

Federated learning has emerged as a promising paradigm for privacy-preserving
collaboration among different parties. Recently, with the popularity of federated learning, an …

Person re-identification (ReID) aims to re-identify a person from non-overlapping camera
views. Since person ReID data contains sensitive personal information, researchers have …

Academia and industry have developed several platforms to support the popular privacy-
preserving distributed learning method—federated learning (FL). However, these platforms …

Current state-of-the-art deep learning based face recognition (FR) models require a large
number of face identities for central training. However, due to the growing privacy …

As a promising distributed technology, federated learning (FL) has been widely used in
vehicular networks involving large amounts of IoT-enabled sensor data, which derives …

Increasingly stringent data privacy regulations limit the development of person re-
identification (ReID) because person ReID training requires centralizing an enormous …

Federated learning (FL) is a machine learning approach that decentralizes data and its
processing by allowing clients to train intermediate models on their devices with locally …

Wind farms are typically located at high latitudes, resulting in a high risk of blade icing. Data-
driven approaches offer promising solutions for blade icing detection, but they rely on a …

Data drift is a thorny challenge when deploying person re-identification (ReID) models into
real-world devices, where the data distribution is significantly different from that of the …