Classical federated learning (FL) enables training machine learning models without sharing
data for privacy preservation, but heterogeneous data characteristic degrades the …

With the digitization of modern cities, large data volumes and powerful computational
resources facilitate the rapid update of intelligent models deployed in smart cities. Continual …

Along with the vast application of Internet of Things (IoT) and the ever-growing concerns
about data protection, a novel type of learning, named incremental federated learning (IFL) …

Lifelong person re-identification (LReID) suffers from the catastrophic forgetting problem
when learning from non-stationary data. Existing exemplar-based and knowledge distillation …

Continuous learning (CL) has recently been adopted into edge video analytics, gaining
huge success in maintaining high accuracy without constantly retraining DNN models by …

Abstract Person Re-identification (ReID) has attracted considerable interests in recent years,
largely driven by the escalating demand for public safety measures. However, the …

Lifelong Person Re-Identification (LReID) aims to continuously learn from successive data
streams, matching individuals across multiple cameras. The key challenge for LReID is how …

The advancement in person re-identification using attribute recognition is constrained by the
increasingly strict data privacy standards since it necessitates the centralization of vast …

Lifelong person re-identification (LReID) refers to matching people across different cameras
given continuous data streams. The challenge of catastrophic forgetting of old knowledge …

With increasing concerns over data privacy and model copyrights, especially in the context
of collaborations between AI service providers and data owners, an innovative Sentinel …