FedPass: Privacy-Preserving Vertical Federated Deep Learning with Adaptive Obfuscation
Authors: Hanlin Gu, Jiahuan Luo, Yan Kang, Lixin Fan, Qiang Yang
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental result s with different datasets and network architectures also justify the superiority of Fed Pass against existing methods in light of its near-optimal tradeoff between privacy and model performance. |
| Researcher Affiliation | Collaboration | Hanlin Gu1 , Jiahuan Luo1 , Yan Kang1 , Lixin Fan1 and Qiang Yang1,2 1Webank, China 2Hong Kong University of Science and Technology, Hong Kong |
| Pseudocode | Yes | Algorithm 1 Fed Pass; Algorithm 2 Adaptive Obfuscation (g()) |
| Open Source Code | No | The paper does not provide any explicit statements or links to open-source code for the described methodology. |
| Open Datasets | Yes | We conduct experiments on three datasets: MNIST [Le Cun et al., 2010], CIFAR10 [Krizhevsky et al., 2014] and Model Net [Wu et al., 2015]. |
| Dataset Splits | No | The paper uses standard datasets but does not explicitly provide specific train/validation/test split percentages, sample counts, or clear references to how these datasets were partitioned for the experiments. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments, such as GPU/CPU models, memory, or cloud computing instances. |
| Software Dependencies | No | The paper does not specify version numbers for any software dependencies, libraries, or programming languages used in the experiments. |
| Experiment Setup | Yes | For Fed Pass, the range of the mean of Gaussian distribution N is from 2 to 200, the variance is from 1 to 64. Passports are embedded in the last convolution layer of the passive party s model and first fully connected layer of the active party s model. ... Input: Communication rounds T, Passive parties number K, learning rate η, batch size b, the passport range and variance {N a, σa} and {N pk, σpk} for the active party and passive party k respectively... |