3 papers
cs.LG2025
FedPhD: Federated Pruning with Hierarchical Learning of Diffusion Models
Qianyu Long, Qiyuan Wang, Christos Anagnostopoulos +1
Federated Learning (FL), as a distributed learning paradigm, trains models over distributed clients' data. FL is particularly beneficial for distributed training of Diffusion Model…
cs.LG2024
Decentralized Personalized Federated Learning based on a Conditional Sparse-to-Sparser Scheme
Qianyu Long, Qiyuan Wang, Christos Anagnostopoulos +1
Decentralized Federated Learning (DFL) has become popular due to its robustness and avoidance of centralized coordination. In this paradigm, clients actively engage in training by…
cs.LG2023
FedDIP: Federated Learning with Extreme Dynamic Pruning and Incremental Regularization
Qianyu Long, Christos Anagnostopoulos, Shameem Puthiya Parambath +1
Federated Learning (FL) has been successfully adopted for distributed training and inference of large-scale Deep Neural Networks (DNNs). However, DNNs are characterized by an extre…