Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Preservation of the Global Knowledge by Not-True Distillation in Federated Learning
Authors: Gihun Lee, Minchan Jeong, Yongjin Shin, Sangmin Bae, Se-Young Yun
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In the experiments, Fed NTD shows state-of-the-art performance on various setups without compromising data privacy or incurring additional communication costs. We test our algorithm on MNIST [11], CIFAR-10 [25], CIFAR-100 [25], and CINIC-10 [10]. We compare our Fed NTD with various existing works, with results shown in Table 1. |
| Researcher Affiliation | Academia | Gihun Lee*, Minchan Jeong*, Yongjin Shin, Sangmin Bae, Se-Young Yun KAIST EMAIL |
| Pseudocode | Yes | Algorithm 1 Federated Not-True Distillation (Fed NTD) |
| Open Source Code | Yes | 1https://github.com/Lee-Gihun/Fed NTD |
| Open Datasets | Yes | We test our algorithm on MNIST [11], CIFAR-10 [25], CIFAR-100 [25], and CINIC-10 [10]. |
| Dataset Splits | No | The paper does not explicitly describe a separate validation dataset split with specific percentages or counts. It primarily refers to training and testing. |
| Hardware Specification | No | The provided text does not specify the hardware used for experiments, such as specific GPU models, CPU types, or memory. |
| Software Dependencies | No | The paper mentions 'Pytorch' in a reference but does not specify any software dependencies with version numbers (e.g., Python version, PyTorch version, specific libraries with versions) used for their experiments. |
| Experiment Setup | Yes | We use a momentum SGD with an initial learning rate of 0.1, and the momentum is set as 0.9. The learning rate is decayed with a factor of 0.99 at each round, and a weight decay of 1e-5 is applied. We adopt two different NIID partition strategies: (i) Sharding [37]: sort the data by label and divide the data into same-sized shards, and control the heterogeneity by s, the number of shards per user. (ii) Latent Dirichlet Allocation (LDA) [34, 46]: assigns partition of class c by sampling pc Dirpαq. |