Federated Few-Shot Class-Incremental Learning
Authors: Muhammad Anwar Masum, Mahardhika Pratama, Lin Liu, H Habibullah, Ryszard Kowalczyk
ICLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our comprehensive experimental results show that UOPP significantly outperforms state-of-the-art (SOTA) methods on three datasets with improvements up to 76% on average accuracy and 90% on harmonic mean accuracy respectively. Our extensive analysis shows UOPP robustness in various numbers of local clients and global rounds, low communication costs, and moderate running time. |
| Researcher Affiliation | Academia | M. Anwar Ma sum , Mahardhika Pratama, Lin Liu, Habibullah Habibullah, and Ryszard Kowalczyk University of South Australia, Mawson Lakes, SA, 5095, Australia EMAIL, EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | A DETAILED PROCESS OF UNIFIED OPTIMIZED PROTOTYPE PROMPT (UOPP) In this section, we present the detailed algorithm of UOPP as shown in algorithm 1. |
| Open Source Code | Yes | The source code of UOPP is publicly available at https://github.com/anwarmaxsum/FFSCIL. |
| Open Datasets | Yes | Datasets: our experiment is done using three benchmarks i.e. split CIFAR100, split Mini Image Net, and split CUB200. The CIFAR100 and mini Image Net datasets contain 100 classes while CUB200 is a dataset of 200 classes. |
| Dataset Splits | Yes | For CIFAR100 and Mini Image Net, we split the dataset into 9 tasks i.e. 60 classes for the base task (t = 0), and 5 classes for each few-shot task (t > 0). We split the CUB200 dataset into 11 tasks i.e. 100 classes for the base task, and 10 classes for each few-shot task. Few shot tasks are measured in 5-shot and 1-shot settings. |
| Hardware Specification | Yes | our numerical study is executed under a single NVIDIA A100 GPU with 40 GB memory across 3 different random seeds. |
| Software Dependencies | No | The paper mentions using a 'Vi T backbone' and refers to 'ODESolver based on Runge-Kutta method' but does not provide specific version numbers for software libraries (e.g., Python, PyTorch, CUDA) as required. |
| Experiment Setup | Yes | The total global round is set to 90 (10 rounds/task) for CIFAR100 and Mini Image Net and 110 for CUB200. For all methods, the local training on each client is set with a maximum of 20 epochs, and the learning rate is set by choosing the best value from {0.001, 5.0} by grid search with 2 incremental factors. For UOPP, the rectification step M is set to 40 steps per iteration. The initial learning rate is set with the best result from 0.001 to 0.2 by a 2 or 5 incremental factor. The prompt length is set to 5. |