19 citations · 36 across the 32 of their papers we have counts for
Showing 2024 · cs.LGShow all
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cs.LG2024
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…
cs.LG2024★ 1 cited
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…
cs.LG2024★ 8 cited
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…