When foundation model meets federated learning: Motivations, challenges, and future directions
Comparative analysis of open-source federated learning frameworks-a literature-based survey and review
Abstract While Federated Learning (FL) provides a privacy-preserving approach to analyze
sensitive data without centralizing training data, the field lacks an detailed comparison of …
sensitive data without centralizing training data, the field lacks an detailed comparison of …
Optimizing performance of federated person re-identification: Benchmarking and analysis
Increasingly stringent data privacy regulations limit the development of person re-
identification (ReID) because person ReID training requires centralizing an enormous …
identification (ReID) because person ReID training requires centralizing an enormous …
Gradient inversion attacks: Impact factors analyses and privacy enhancement
Gradient inversion attacks (GIAs) have posed significant challenges to the emerging
paradigm of distributed learning, which aims to reconstruct the private training data of clients …
paradigm of distributed learning, which aims to reconstruct the private training data of clients …