Federated Model Distillation with Noise-Free Differential Privacy
Authors: Lichao Sun, Lingjuan Lyu
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our extensive experimental results on various datasets validate that FEDMD-NFDP can deliver not only comparable utility and communication efficiency but also provide a noise-free differential privacy guarantee. |
| Researcher Affiliation | Collaboration | Lichao Sun1 , Lingjuan Lyu2 1 Lehigh University 2 Ant Group |
| Pseudocode | Yes | Algorithm 1 FEDMD-NFDP. Initialization phase does not involve collaboration. Di and wi are local dataset and model parameters from i-th party. Y i p[t] is the prediction from i-th party on the chosen public subset Xp Dp in round t. |
| Open Source Code | No | The paper states that experiments are implemented using PyTorch but does not provide any explicit statement about open-sourcing its own code or a link to a repository. |
| Open Datasets | Yes | In the experiment, we evaluate on paired datasets, i.e., MNIST/FEDMNIST and CIFAR-10/CIFAR-100. For MNIST/FEMNIST, the public data is the MNIST, and the private data is a subset of the Federated Extended MNIST (FEMNIST) [Caldas et al., 2018]... For CIFAR-10/CIFAR-100, the public dataset is the CIFAR-10, and the private dataset is a subset of the CIFAR-100 [Sun et al., 2021]. |
| Dataset Splits | No | The paper mentions training on 'sampled subset from private local data' and 'unlabeled public set' but does not provide specific details on train/validation/test splits, percentages, or sample counts, nor does it explicitly refer to a 'validation' set for model selection. |
| Hardware Specification | Yes | A single GPU NVIDIA Tesla V100 is used in the experiments. |
| Software Dependencies | No | The paper states 'All experiments are implemented by using Pytorch' but does not specify a version number for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | In each communication round, we use a subset of size 5000 that is randomly selected from the entire public dataset... We empirically choose R = 20, T1 = 20, T2 = 2, T3 = 1 via grid search. We initialize all parties with the same pre-trained model on some labelled data in the same domain. and Each party s local model is two or three-layer deep neural networks for both MNIST/FEMNIST and CIFAR-10/CIFAR-100. |