Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Hot-pluggable Federated Learning: Bridging General and Personalized FL via Dynamic Selection
Authors: Lei Shen, Zhenheng Tang, Lijun Wu, Yonggang Zhang, Xiaowen Chu, Tao Qin, Bo Han
ICLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our comprehensive experiments and ablation studies demonstrate that HPFL significantly outperforms state-of-the-art GFL and PFL algorithms. Additionally, we empirically show HPFL s remarkable potential to resolve other practical FL problems such as continual federated learning and discuss its possible applications in one-shot FL, anarchic FL, and FL plugin market. Our work is the first attempt towards improving GFL performance through a selecting mechanism with personalized plug-ins. |
| Researcher Affiliation | Collaboration | Lei Shen1, Zhenheng Tang2, Lijun Wu3 Yonggang Zhang1 Xiaowen Chu 2,4 Tao Qin5 Bo Han1, 1 TMLR Group, Department of Computer Science, Hong Kong Baptist University 2 CSE Department, The Hong Kong University of Science and Technology 3 Shanghai AI Laboratory 4 DSA Thrust, The Hong Kong University of Science and Technology (Guangzhou) 5 Microsoft Research AI4Science |
| Pseudocode | Yes | Algorithm 1 HPFL. |
| Open Source Code | Yes | Our code is released at https://github.com/tmlr-group/HPFL. |
| Open Datasets | Yes | We conduct experiments on four commonly used image classification datasets in FL, including CIFAR-10 (Krizhevsky et al., 2009), CIFAR-100 (Krizhevsky et al., 2009), Fashion-MNIST (Xiao et al., 2017), and Tiny-Image Net (Le & Yang, 2015) |
| Dataset Splits | Yes | We conduct experiments on four commonly used image classification datasets in FL, including CIFAR-10 (Krizhevsky et al., 2009), CIFAR-100 (Krizhevsky et al., 2009), Fashion-MNIST (Xiao et al., 2017), and Tiny-Image Net (Le & Yang, 2015), with Latent Dirichlet Sampling (Dir) partition method (α = 0.1, 0.05) to simulate data heterogeneity following (He et al., 2020b; Li et al., 2021b; Luo et al., 2021; Tang et al., 2022b). ... To make the training data and test data of a client have the same distribution following the settings of most PFL methods (Collins et al., 2021), we count the number of samples Strain(c, m) in each class c of training data of client m and split test data of that clients in that distribution... |
| Hardware Specification | Yes | We conduct experiments using NVIDIA A100 40GB GPU, AMD EPYC 7742 64-Core Processor Units. The operating system is Ubuntu 20.04.1 LTS. The pytorch version is 1.12.1. The numpy version is 1.23.2. The cuda version is 12.0. |
| Software Dependencies | Yes | The pytorch version is 1.12.1. The numpy version is 1.23.2. The cuda version is 12.0. |
| Experiment Setup | Yes | We use SGD without momentum as the optimizer for all experiments, with a batch size of 128 and weight decay of 0.0001. The learning rate is set as 0.1 for both the training of the global model and the fine-tuning on local datasets. The main results shown in Tabel 2 are conducted with 1-layer plug-ins (i.e. only classifier). We run all algorithms for 1000 communication rounds, with 1 local epoch per round. |