FedP3: Federated Personalized and Privacy-friendly Network Pruning under Model Heterogeneity
Authors: Kai Yi, Nidham Gazagnadou, Peter Richtárik, Lingjuan Lyu
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 EXPERIMENTS and subsequent subsections clearly outline empirical studies with data analysis and performance metrics. For example, We utilize benchmark datasets CIFAR10/100 Krizhevsky et al. (2009), a subset of EMNIST labeled EMNIST-L Cohen et al. (2017), and Fashion MNIST Xiao et al. (2017), maintaining standard train/test splits as in Mc Mahan et al. (2017) and Li et al. (2020b). and Figure 2 presents a comparison of different layer overlapping strategies. |
| Researcher Affiliation | Collaboration | 1KAUST, 2Sony AI |
| Pseudocode | Yes | Algorithm 1 Fed P3 |
| Open Source Code | No | The paper mentions Given that the only partially open-source code available is from FjORD, referring to a third-party project, but does not provide any statement or link indicating that the authors' own source code for Fed P3 is publicly available. |
| Open Datasets | Yes | We utilize benchmark datasets CIFAR10/100 Krizhevsky et al. (2009), a subset of EMNIST labeled EMNIST-L Cohen et al. (2017), and Fashion MNIST Xiao et al. (2017), maintaining standard train/test splits as in Mc Mahan et al. (2017) and Li et al. (2020b). |
| Dataset Splits | No | While CIFAR100 has 100 labels, the others have 10, with a consistent data split of 70% for training and 30% for testing. The paper specifies train/test splits but does not explicitly mention a separate validation split. |
| Hardware Specification | Yes | Our experiments were conducted on NVIDIA A100 or V100 GPUs, depending on their availability in our cluster. |
| Software Dependencies | Yes | The framework was implemented in Py Torch 1.4.0 and torchvision 0.5.0 within a Python 3.8 environment. |
| Experiment Setup | Yes | We standardized the experiments to 500 epochs with a local training batch size of 48. The number of local updates was set at 10 to assess final performance. For the learning rate, we conducted a grid search, exploring a range from 10 5 to 0.1, with a fivefold increase at each step. |