On Bridging Generic and Personalized Federated Learning for Image Classification
Authors: Hong-You Chen, Wei-Lun Chao
ICLR 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate FED-ROD on multiple datasets under various non-IID settings. FED-ROD consistently outperforms existing generic and personalized FL algorithms in both setups. |
| Researcher Affiliation | Academia | Hong-You Chen The Ohio State University, USA Wei-Lun Chao The Ohio State University, USA |
| Pseudocode | Yes | In algorithm 1 and algorithm 2, we provide pseudocode of our FED-ROD algorithm. Algorithm 1: FED-ROD (linear) (Federated Robust Decoupling) [...] Algorithm 2: FED-ROD (hyper) (Federated Robust Decoupling) |
| Open Source Code | Yes | We also provide our code in https://github.com/hongyouc/Fed-RoD. |
| Open Datasets | Yes | Datasets, models, and settings. We use CIFAR-10/100 (Krizhevsky et al., 2009) and Fashion MNIST (FMNIST) (Xiao et al., 2017). |
| Dataset Splits | Yes | The best personalized model after local training is selected for each client using a validation set. |
| Hardware Specification | Yes | We run our experiments on four Ge Force RTX 2080 Ti GPUs with Intel i9-9960X CPUs. |
| Software Dependencies | No | The paper mentions using "SGD optimizer" but does not specify software versions for libraries or frameworks (e.g., Python, PyTorch, TensorFlow, with version numbers). |
| Experiment Setup | Yes | We train every FL algorithm for 100 rounds, with 5 local epochs in each round. We initialize the model weights from normal distributions. As mentioned in (Li et al., 2020b), the local learning rate must decay along the communication rounds. We initialize it with 0.01 and decay it by 0.99 every round, similar to (Acar et al., 2021). Throughout the experiments, we use the SGD optimizer with weight decay 1e 5 and a 0.9 momentum. The mini-batch size is 40 (16 for EMNIST). |