116 citations · 236 across the 60 of their papers we have counts for
9 papers · 1 filter
Siamese Machine Unlearning with Knowledge Vaporization and Concentration
Songjie Xie, Hengtao He, Shenghui Song +2
In response to the practical demands of the ``right to be forgotten" and the removal of undesired data, machine unlearning emerges as an essential technique to remove the learned k…
Federated Low-Rank Adaptation with Differential Privacy over Wireless Networks
Tianqu Kang, Zixin Wang, Hengtao He +3
Fine-tuning large pre-trained foundation models (FMs) on distributed edge devices presents considerable computational and privacy challenges. Federated fine-tuning (FedFT) mitigate…
The Effect of Quantization in Federated Learning: A Rényi Differential Privacy Perspective
Tianqu Kang, Lumin Liu, Hengtao He +3
Federated Learning (FL) is an emerging paradigm that holds great promise for privacy-preserving machine learning using distributed data. To enhance privacy, FL can be combined with…
Binary Federated Learning with Client-Level Differential Privacy
Lumin Liu, Jun Zhang, Shenghui Song +1
Federated learning (FL) is a privacy-preserving collaborative learning framework, and differential privacy can be applied to further enhance its privacy protection. Existing FL sys…
Fairness-aware Federated Minimax Optimization with Convergence Guarantee
Gerry Windiarto Mohamad Dunda, Shenghui Song
Federated learning (FL) has garnered considerable attention due to its privacy-preserving feature. Nonetheless, the lack of freedom in managing user data can lead to group fairness…
Local SGD Accelerates Convergence by Exploiting Second Order Information of the Loss Function
Linxuan Pan, Shenghui Song
With multiple iterations of updates, local statistical gradient descent (L-SGD) has been proven to be very effective in distributed machine learning schemes such as federated learn…