Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learning
Authors: Wonyong Jeong, Jaehong Yoon, Eunho Yang, Sung Ju Hwang
ICLR 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through extensive experimental validation of our method in the two different scenarios, we show that our method outperforms both local semi-supervised learning and baselines which naively combine federated learning with semi-supervised learning. |
| Researcher Affiliation | Collaboration | Wonyong Jeong1, Jaehong Yoon2, Eunho Yang1,3, and Sung Ju Hwang1,3 Graduate School of AI1, KAIST, Seoul, South Korea School of Computing2, KAIST, Daejeon, South Korea AITRICS 3, Seoul, South Korea {wyjeong, jaehong.yoon, eunhoy, sjhwang82}@kaist.ac.kr |
| Pseudocode | Yes | Algorithm 1 Labels-at-Client Scenario; Algorithm 2 Labels-at-Server Scenario |
| Open Source Code | Yes | The code is available at https://github.com/wyjeong/Fed Match. |
| Open Datasets | Yes | We use CIFAR-10 for this task and split 60, 000 instances into training (54, 000), valid (3, 000), and test (3, 000) sets... We use Fashion-MNIST dataset for this task |
| Dataset Splits | Yes | We use CIFAR-10 for this task and split 60, 000 instances into training (54, 000), valid (3, 000), and test (3, 000) sets. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions software components like 'SGD' and 'Res Net-9 networks' but does not specify version numbers for any libraries, frameworks (e.g., PyTorch, TensorFlow), or programming languages. |
| Experiment Setup | Yes | Table 4: Hyper-Parameters & Training Setups We provide all hyper-parameters and training setups for all baseline models and our method. Detailed hyper-parameters are also available in the code. (Includes learning rate, weight decay, batch sizes, etc.) |