Convergence Analysis of Split Federated Learning on Heterogeneous Data
Authors: Pengchao Han, Chao Huang, Geng Tian, Ming Tang, Xin Liu
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental experiments validate our theoretical results and show that SFL outperforms FL and split learning (SL) when data is highly heterogeneous across a large number of clients. |
| Researcher Affiliation | Academia | Pengchao Han Guangdong University of Technology, China hanpengchao@gdut.edu.cn Chao Huang Montclair State University, USA huangch@montclair.edu Geng Tian Southern University of Science and Technology, China 12332463@mail.sust.edu.cn Ming Tang Southern University of Science and Technology, China tangm3@sust.edu.cn Xin Liu University of California, Davis, USA xinliu@ucdavis.edu |
| Pseudocode | Yes | Algorithm 1: SFL-V1 under clients partial participation; Algorithm 2: SFL-V2 under clients partial participation |
| Open Source Code | Yes | Our codes are provided in https://github.com/TIANGeng708/ Convergence-Analysis-of-Split-Federated-Learning-on-Heterogeneous-Data. |
| Open Datasets | Yes | We conduct experiments on CIFAR-10 and CIFAR-100 [13]. More experiments on FEMNIST are given in Appendix I.5. |
| Dataset Splits | No | The paper mentions training parameters and local epochs but does not specify validation dataset splits (e.g., percentages or counts) or reference standard validation splits. |
| Hardware Specification | Yes | The experiments are run on a CPU (Intel(R) Xeon(R) Gold 5320 at 2.20GHz) and a GPU (A100-PCIE-80GB). |
| Software Dependencies | No | The paper mentions the use of ResNet-18 as a model structure, learning rates, and batch sizes, but it does not specify any software libraries or frameworks with their version numbers (e.g., PyTorch 1.x, TensorFlow 2.x, Python 3.x). |
| Experiment Setup | Yes | The learning rates for SFL-V1, SFL-V2, FL, and SL are set as 0.01. The batchsize bs is 128, and we run experiments for T = 200 rounds. Unless stated otherwise, we use N = 10, β = 0.1, E = 5, where E is the number of local epochs for client-side model aggregation (i.e., every E times of training performed over each client s dataset, their client-side models are aggregated at the fed server), and hence τ = Dn / bs E. We set τ = τ for the fair comparison to vanilla FL. |