FedSSI: Rehearsal-Free Continual Federated Learning with Synergistic Synaptic Intelligence
Authors: Yichen Li, Yuying Wang, Haozhao Wang, Yining Qi, Tianzhe Xiao, Ruixuan Li
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show that Fed SSI achieves superior performance compared to stateof-the-art methods. We conduct extensive experiments on various datasets and different CFL task scenarios. Experimental results show that our proposed model outperforms state-of-the-art methods by up to 12.47% in terms of final accuracy on different tasks. 5. Experiments |
| Researcher Affiliation | Academia | 1School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China 2School of Computer Science and Technology, Soochow University, Suzhou, China. Correspondence to: Ruixuan Li <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Fed SSI Input: T: communication round; K: client number; η: learning rate; {T t}n t=1: distributed dataset with n tasks; w: parameter of the model; vt k: personalized surrogate model in client k for the t-th task; sl k,i: contribution of the i-th parameter in client k with t-th task. Output: w1, w2, . . . , wk: target classification model. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code, nor does it provide a link to a code repository or mention code in supplementary materials. |
| Open Datasets | Yes | Datasets. We conduct our experiments with heterogeneously partitioned datasets across two federated incremental learning scenarios using six datasets: (1) Class Incremental Learning: CIFAR10 (Krizhevsky et al., 2009), CIFAR100 (Krizhevsky et al., 2009), and Tiny-Image Net (Le & Yang, 2015); (2) Domain-Incremental Learning: Digit10, Office31 (Saenko et al., 2010), and Office-Caltech10 (Zhang & Davison, 2020). |
| Dataset Splits | Yes | CIFAR10: A dataset with 10 object classes... It consists of 50,000 training images and 10,000 test images. CIFAR100: Similar to CIFAR10, but with 100 fine-grained object classes. It has 50,000 training images and 10,000 test images. Tiny-Image Net: A subset of the Image Net dataset with 200 object classes. It contains 100,000 training images, 10,000 validation images, and 10,000 test images. MNIST: A dataset of handwritten digits with a training set of 60,000 examples and a test set of 10,000 examples. |
| Hardware Specification | Yes | All experiments are run on 8 RTX 4090 GPUs and 16 RTX 3090 GPUs. |
| Software Dependencies | No | The paper does not explicitly state any specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow, or CUDA versions). |
| Experiment Setup | Yes | Table 5. Experimental Details. Settings of different datasets in the experiments section. Attributes CIFAR10 CIFAR100 Tiny-Image Net Digit10 Office31 Office-Caltech-10 Task size 178MB 178MB 435MB 480M 88M 58M Image number 60K 60K 120K 110K 4.6k 2.5k Image Size 3 32 32 3 32 32 3 64 64 1 28 28 3 300 300 3 300 300 Task number n = 5 n = 10 n = 10 n = 4 n = 3 n = 4 Task Scenario Class-IL Class-IL Class-IL Domain-IL Domain-IL Domain-IL Batch Size s = 64 s = 64 s =128 s = 64 s = 32 s = 32 ACC metrics Top-1 Top-1 Top-10 Top-1 Top-1 Top-1 Learning Rate l = 0.01 l = 0.01 l = 0.001 l = 0.001 l = 0.01 l = 0.01 Data heterogeneity α = 0.1 α = 1.0 α = 10.0 α = 0.1 α = 1.0 α = 1.0 Client numbers C = 20 C=20 C=20 C=15 C=10 C=8 Local training epoch E = 20 E = 20 E = 20 E = 20 E = 20 E = 15 Client selection ratio k = 0.4 k = 0.5 k = 0.6 k = 0.4 k = 0.4 k = 0.5 Communication Round T = 80 T = 100 T = 100 T = 60 T = 60 T = 40 |