Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learning
Authors: Wonyong Jeong, Jaehong Yoon, Eunho Yang, Sung Ju Hwang
ICLR 2021 | Venue 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 EMAIL |
| 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.) |