3 papers
cs.LG2025
Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data
Abhijit Chunduru, Majid Morafah, Mahdi Morafah +2
The inevitable presence of data heterogeneity has made federated learning very challenging. There are numerous methods to deal with this issue, such as local regularization, better…
cs.CV2024
Boosting Adversarial Transferability with Low-Cost Optimization via Maximin Expected Flatness
Chunlin Qiu, Ang Li, Yiheng Duan +4
Transfer-based attacks craft adversarial examples on white-box surrogate models and directly deploy them against black-box target models, offering model-agnostic and query-free thr…
cs.LG2024
Enhancing One-Shot Federated Learning Through Data and Ensemble Co-Boosting
Rong Dai, Yonggang Zhang, Ang Li +3
One-shot Federated Learning (OFL) has become a promising learning paradigm, enabling the training of a global server model via a single communication round. In OFL, the server mode…