Convergence Analysis of Split Federated Learning on Heterogeneous Data
Authors: Pengchao Han, Chao Huang, Geng Tian, Ming Tang, Xin Liu
NeurIPS 2024 | Venue 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 EMAIL Chao Huang Montclair State University, USA EMAIL Geng Tian Southern University of Science and Technology, China EMAIL Ming Tang Southern University of Science and Technology, China EMAIL Xin Liu University of California, Davis, USA EMAIL |
| 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. |