MultiSFL: Towards Accurate Split Federated Learning via Multi-Model Aggregation and Knowledge Replay
Authors: Zeke Xia, Ming Hu, Dengke Yan, Ruixuan Liu, Anran Li, Xiaofei Xie, Mingsong Chen
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results obtained from various non-IID and IID scenarios demonstrate that Multi SFL significantly outperforms conventional SFL methods by up to a 23.25% test accuracy improvement. |
| Researcher Affiliation | Academia | 1Mo E Engineering Research Center of SW/HW Co-Design Tech. and App., East China Normal University, China 2School of Computing and Information Systems, Singapore Management University, Singapore 3Department of Biomedical Informatics & Data Science, School of Medicine, Yale University |
| Pseudocode | Yes | Algorithm 1 details the implementation of our Multi SFL approach. |
| Open Source Code | No | The paper does not contain any explicit statement regarding the release of source code for the methodology described, nor does it provide a direct link to a code repository. |
| Open Datasets | Yes | We compared Multi SFL with all baselines on four well-known datasets, i.e., CIFAR-10, CIFAR-100 (Krizhevsky 2009), FEMNIST (Caldas et al. 2018), and Tiny Image Net (Deng et al. 2009). |
| Dataset Splits | No | The paper describes how non-IID distributions were created using the Dirichlet distribution for CIFAR-10, CIFAR-100, and Tiny-Image Net, and mentions that FEMNIST is naturally non-IID. It also specifies client selection for federated learning rounds (10% of 100 clients). However, it does not provide specific train/validation/test dataset splits (e.g., percentages or sample counts) for reproducibility. |
| Hardware Specification | Yes | All experimental results were obtained from an Ubuntu workstation equipped with an Intel i9 CPU, 64GB of memory, and an NVIDIA RTX 4090 GPU. |
| Software Dependencies | No | The paper states, "We implemented Multi SFL using the Py Torch framework (Paszke et al. 2019)", but it does not provide a specific version number for PyTorch or any other software dependency used in their implementation. |
| Experiment Setup | Yes | For all methods, we adopted an SGD optimizer with a fixed learning rate of 0.01 and a momentum of 0.5 and set the batch size to 50. For our method, we set α to 0.1 and γ to 0.5. |