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..
Few-Round Learning for Federated Learning
Authors: Younghyun Park, Dong-Jun Han, Do-Yeon Kim, Jun Seo, Jaekyun Moon
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results show that our method generalizes well for arbitrary groups of clients and provides large performance improvements given the same overall communication/computation resources, compared to other baselines relying on known pretraining methods. |
| Researcher Affiliation | Academia | Younghyun Park EMAIL Dong-Jun Han EMAIL Do-Yeon Kim EMAIL Jun Seo EMAIL Jaekyun Moon EMAIL School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST) |
| Pseudocode | Yes | Algorithm 1 Proposed Meta-Training Algorithm for Few-Round Learning |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code availability. |
| Open Datasets | Yes | We validate our algorithm on CIFAR-100 [10], mini Image Net [19], FEMNIST[2]. |
| Dataset Splits | Yes | Following the data splits in [14], for CIFAR-100 and mini Image Net, 100 classes are divided into 64 train, 16 validation and 20 test classes. |
| Hardware Specification | Yes | All methods are implemented using Pytorch and trained with a single Ge Force RTX 2080 Ti. |
| Software Dependencies | No | The paper mentions 'Pytorch' but does not specify a version number or other software dependencies with version information. |
| Experiment Setup | Yes | We adopt the SGD optimizer with a learning rate of β = 0.001 for the meta-learner and a learning rate of α = 0.0001 for the learner. We set the mini-batch size to 60 and the number of local epochs at each client to E = 1. |