FedABC: Targeting Fair Competition in Personalized Federated Learning
Authors: Dui Wang, Li Shen, Yong Luo, Han Hu, Kehua Su, Yonggang Wen, Dacheng Tao
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments are conducted on two popular datasets under different settings, and the results demonstrate that our Fed ABC can significantly outperform the existing counterparts. |
| Researcher Affiliation | Collaboration | 1 National Engineering Research Center for Multimedia Software, School of Computer Science, Institute of Artificial Intelligence and Hubei Key Laboratory of Multimedia and Network Communication Engineering, Wuhan University, China, 2 Hubei Luojia Laboratory, Wuhan, China, 3 JD Explore Academy, China, 4 School of Information and Electronics, Beijing Institute of Technology, China, 5 School of Computer Science and Engineering, Nanyang Technological University, Singapore |
| Pseudocode | Yes | Algorithm 1: Federated Averaging via Binary classification |
| Open Source Code | No | The paper does not provide any concrete access to source code, such as a repository link or an explicit statement of code release. |
| Open Datasets | Yes | We use MNIST (Lecun and Bottou 1998) and CIFAR-10 (Krizhevsky and Hinton 2009) as benchmarks. To simulate the heterogeneous federated learning scenario, we follow the previous works (Yurochkin et al. 2019; Wang et al. 2020) that utilize Dirichlet distribution Dir(α) to partition the training dataset and generate the corresponding test data for each client following the same distribution |
| Dataset Splits | No | The paper mentions partitioning the training dataset and generating test data but does not explicitly describe a separate validation split, its size, or how it was formed. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions using SGD optimizer but does not list specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | Our method has four hyper-parameters: mp,mn,mnn and σ. ... For CIFAR-10, we set them as 0.85, 0.2, 0.3, and 2, respectively. For MNIST, we set them as 0.75, 0.25, 0.3, and 2, respectively. ... We use the SGD optimizer with weight decay 1e 5 and a 0.9 momentum and the bath size is 64. For MNIST, the learning rate is 0.01. For CIFAR-10, the learning rate is 0.1. We train every method for 100 rounds and 200 rounds on MNIST and CIFAR-10, respectively. For the federated framework setting, the participation rate of clients is set as 0.5, which means that random 10 clients will be activated in each communication round. The local training epochs are set as 5 for all the experiments. |