Collaborative Learning by Detecting Collaboration Partners
Authors: Shu Ding, Wei Wang
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on real-world datasets verify the effectiveness of our method. |
| Researcher Affiliation | Academia | Shu Ding, Wei Wang National Key Laboratory for Novel Software Technology Nanjing University, Nanjing 210023, China {dings, wangw}@lamda.nju.edu.cn |
| Pseudocode | Yes | Algorithm 1 Adaptive Collaborative Learning with Modularity Maximization (ACLMM); Algorithm 2 Adaptive Collaborative Learning with Clustering (ACLC) |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository. |
| Open Datasets | Yes | The MNIST dataset consists of 70K handwritten digit images in 10 classes; The CIFAR-10/100 dataset consists of 60K color images in 10/100 classes; The Federated Extended MNIST (FEMNIST) dataset is built by partitioning the data in Extended MNIST based on the writer of the digit/character. |
| Dataset Splits | Yes | The MNIST dataset consists of 70K handwritten digit images in 10 classes, which has a training set of 60K examples and a test set of 10K examples. They have 50K training images and 10K test images. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used for running its experiments, such as specific GPU or CPU models. |
| Software Dependencies | No | The paper mentions using CNN models and ResNet-18, but does not provide specific software dependency versions (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | We set N = 10 and M = 2000 for MNIST. We set N = 20 and M = 5000 for CIFAR-10 and set N = 50 and M = 20000 for CIFAR-100. We further set the number of groups K = 3 for MNIST, K = 4 for CIFAR-10, K = 7 for CIFAR-100 and K = 4 for FEMNIST. |