Few-Round Learning for Federated Learning
Authors: Younghyun Park, Dong-Jun Han, Do-Yeon Kim, Jun Seo, Jaekyun Moon
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | 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 dnffkf369@kaist.ac.kr Dong-Jun Han djhan93@kaist.ac.kr Do-Yeon Kim dy.kim@kaist.ac.kr Jun Seo tjwns0630@kaist.ac.kr Jaekyun Moon jmoon@kaist.edu 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. |