Dynamic Personalized Federated Learning with Adaptive Differential Privacy
Authors: Xiyuan Yang, Wenke Huang, Mang Ye
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on CIFAR-10, FEMNIST and SVHN dataset demonstrate the effectiveness of our approach in achieving better performance and robustness against clipping, under personalized federated learning with differential privacy. |
| Researcher Affiliation | Academia | National Engineering Research Center for Multimedia Software, Institute of Artificial Intelligence, Hubei Key Laboratory of Multimedia and Network Communication Engineering, School of Computer Science, Hubei Luojia Laboratory, Wuhan University, Wuhan, China. |
| Pseudocode | Yes | Algorithm 1: The Proposed Method: Fed DPA |
| Open Source Code | Yes | https://github.com/xiyuanyang45/Dynamic PFL |
| Open Datasets | Yes | Our method is evaluated on two classification tasks, FEMNIST [6], CIFAR-10 [21] and SVHN [34], embodying real-world non-IID and privacy-constrained scenarios. |
| Dataset Splits | No | No specific train/validation/test splits (e.g., percentages or absolute counts) are explicitly provided. The paper mentions partitioning CIFAR-10 into 10 subsets via Dirichlet distribution for non-IID data among clients, and that accuracy is measured on clients' respective datasets, but does not detail how each client's dataset is split for training, validation, and testing. |
| Hardware Specification | Yes | All experiments were implemented in Python with Py Torch on an NVIDIA 3090 GPU. |
| Software Dependencies | No | The paper mentions 'Python with Py Torch' and 'Opacus' but does not specify version numbers for these software components. For example, it does not state 'PyTorch 1.9' or 'Opacus X.Y.Z'. |
| Experiment Setup | Yes | For all dataset FEMNIST, CIFAR-10 and SVHN, we set the learning rate to 1e-3 and optimize hyperparameters τ, λ1, and λ2 through grid search in {0.05, 0.1, 0.3, 0.5}. We use global epochs of 30 and 40, local epochs of 3 and 4, and batch sizes of 16 and 64 for FEMNIST and CIFAR-10, respectively. |