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..
FedTrans: Client-Transparent Utility Estimation for Robust Federated Learning
Authors: Mingkun Yang, Ran Zhu, Qing Wang, Jie Yang
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our evaluation results demonstrate that leveraging Fed Trans to select the clients can improve the accuracy performance (up to 7.8%), ensuring the robustness of FL in noisy scenarios 1. 1 INTRODUCTION |
| Researcher Affiliation | Academia | Mingkun Yang , Ran Zhu , Qing Wang, Jie Yang Department of Software Technology Delft University of Technology EMAIL |
| Pseudocode | Yes | Algorithm 1 Variational Utility Inference Require: Local updates {W i,j}j J i, global model W i 1, Round-Reputation Matrix R, Server auxiliary dataset Da |
| Open Source Code | Yes | Code is available at https://github.com/Ran-ZHU/Fed Trans |
| Open Datasets | Yes | We use two widely-used image datasets: CIFAR10 (Krizhevsky et al., 2009) and Fashion-MNIST (FMNIST) (Xiao et al., 2017). |
| Dataset Splits | No | The paper mentions non-IID and IID settings and how data is distributed among clients, but does not provide specific train/validation/test dataset splits (e.g., percentages or sample counts) for reproducibility. |
| Hardware Specification | Yes | We implement all the comparison methods in Python and the neural networks with Py Torch, running on an NVIDIA 2080Ti GPU. |
| Software Dependencies | No | The paper mentions using Python and PyTorch for implementation but does not specify version numbers for these or other software dependencies. |
| Experiment Setup | Yes | In local training, local epochs are set to 5 and the learning rate is 1e 2. We use SGD with momentum factor = 0.9 as the local optimizer. We adopt f Wd with Multi-Layer Perception (MLP) having 2 hidden layers of 128 and 64 dimensions respectively. In discriminator training, we select the learning rate as 1e 3, and we set the priors A and B by sampling from a uniform distribution [0, 10] and update them in E-step according to Theorem 2.1 and Theorem 2.2. |