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..
Dual Calibration-based Personalised Federated Learning
Authors: Xiaoli Tang, Han Yu, Run Tang, Chao Ren, Anran Li, Xiaoxiao Li
IJCAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on widely used benchmark datasets demonstrate that DC-PFL outperforms eight state-of-the-art methods, surpassing the best-performing baseline by 1.22% and 9.22% in terms of accuracy on datasets CIFAR-10 and CIFAR-100, respectively. |
| Researcher Affiliation | Academia | 1College of Computing and Data Science, Nanyang Technological University, Singapore 2South China University of Technology, China 3Department of Electrical and Computer Engineering, The University of British Columbia, Canada |
| Pseudocode | Yes | Algorithm 1 DC-PFL |
| Open Source Code | No | The paper does not contain an explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We assess the performance of the proposed DC-PFL alongside baselines on datasets CIFAR-10 and CIFAR-100^1. ... 1https://www.cs.toronto.edu/ kriz/cifar.html |
| Dataset Splits | Yes | Furthermore, the data from each client is partitioned into three distinct subsets: training, evaluation, and testing, with an 8:1:1 allocation ratio. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) needed to replicate the experiment. |
| Experiment Setup | Yes | We optimize FL hyperparameters through an extensive grid search by adjusting the batch size for local training from {32, 64, 128, 256, 512} and the number of local training epochs from {1, 10, 30, 50, 100}. We utilize the SGD optimizer with a fixed learning rate (η) of 0.01 for both local training and global classifier training. The total number of communication rounds (T) is set to 100 on CIFAR-10 and to 500 on CIFAR100 to ensure convergence across all algorithms. |