Exploring Vacant Classes in Label-Skewed Federated Learning
Authors: Kuangpu Guo, Yuhe Ding, Jian Liang, Zilei Wang, Ran He, Tieniu Tan
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments validate the efficacy of Fed VLS, demonstrating superior performance compared to previous state-of-the-art (SOTA) methods across diverse datasets with varying degrees of label skews. |
| Researcher Affiliation | Academia | 1University of Science and Technology of China 2NLPR & MAIS, Institute of Automation, Chinese Academy of Sciences 3Anhui University 4University of Chinese Academy of Sciences 5Nanjing University EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 in the technical appendix shows the overflow of our method. |
| Open Source Code | Yes | Code https://github.com/krumpguo/Fed VLS |
| Open Datasets | Yes | We evaluate the effectiveness of our approach across various image classification datasets, including MNIST (Deng 2012), CIFAR10 (Krizhevsky 2009), CIFAR100 (Krizhevsky 2009), and Tiny Image Net (Le and Yang 2015). |
| Dataset Splits | No | The paper states: "We partitioned each dataset into distinct training and test sets. Subsequently, the training set undergoes further division into non-overlapping subsets, distributed among different clients." However, it does not provide specific percentages or counts for the global training/test/validation splits for these datasets, nor does it cite a source for such standard splits. |
| Hardware Specification | No | The paper mentions the network architectures used (Mobile Net V2, DNN for MNIST) but does not provide any specific hardware details such as GPU models, CPU models, or memory. |
| Software Dependencies | No | The paper mentions using stochastic gradient descent (SGD) optimization but does not specify any software libraries or their version numbers (e.g., Python, PyTorch, TensorFlow, CUDA). |
| Experiment Setup | Yes | We set the number of clients N to 10 and implement full client participation. We run 100 communication rounds for all experiments on the CIFAR10/100 datasets and 50 communication rounds on the MNIST and Tiny Image Net datasets. Within each communication round, local training spans 5 epochs for MNIST and 10 epochs for the other datasets. For Fed Concat (Diao, Li, and He 2024) and Fed GF (Lee and Yoon 2024), we followed the original paper s settings for communication rounds and local epochs. We employ stochastic gradient descent (SGD) optimization with a learning rate of 0.01, a momentum of 0.9, and a batch size of 64. Weight decay is set to 10 5 for MNIST and CIFAR10 and 10 4 for CIFAR100 and Tiny Image Net. The hyperparameter λ of Fed VLS in Equation 6 is set to 0.1 for MNIST and CIFAR10, while it is set to 0.5 for CIFAR100 and Tiny Image Net. |