FedLAP-DP: Federated Learning by Sharing Differentially Private Loss
Approximations

FedLAP-DP: Federated Learning by Sharing Differentially Private Loss Approximations

2 February 2023

Raouf Kerkouche

Mario Fritz

Papers citing "FedLAP-DP: Federated Learning by Sharing Differentially Private Loss Approximations"

6 / 6 papers shown

Title
Language Models as Zero-shot Lossless Gradient Compressors: Towards General Neural Parameter Prior Models Hui-Po Wang Mario Fritz 28 3 0 26 Sep 2024
Generalizing Dataset Distillation via Deep Generative Prior George Cazenavette Tongzhou Wang Antonio Torralba Alexei A. Efros Jun-Yan Zhu DD 91 84 0 02 May 2023
Differentially Private Fine-tuning of Language Models Da Yu Saurabh Naik A. Backurs Sivakanth Gopi Huseyin A. Inan ... Y. Lee Andre Manoel Lukas Wutschitz Sergey Yekhanin Huishuai Zhang 134 344 0 13 Oct 2021
Label Leakage and Protection in Two-party Split Learning Oscar Li Jiankai Sun Xin Yang Weihao Gao Hongyi Zhang Junyuan Xie Virginia Smith Chong-Jun Wang FedML 122 139 0 17 Feb 2021
Dataset Condensation with Differentiable Siamese Augmentation Bo-Lu Zhao Hakan Bilen DD 189 288 0 16 Feb 2021
FedBN: Federated Learning on Non-IID Features via Local Batch Normalization Xiaoxiao Li Meirui Jiang Xiaofei Zhang Michael Kamp Qi Dou OOD FedML 168 770 0 15 Feb 2021