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..
Personalized Federated Learning with Contextualized Generalization
Authors: Xueyang Tang, Song Guo, Jingcai Guo
IJCAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on multiple realworld datasets show that our approach surpasses the state-of-the-art methods on test accuracy by a significant margin. |
| Researcher Affiliation | Academia | 1Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China 2The Hong Kong Polytechnic University Shenzhen Research Institute, Shenzhen, China |
| Pseudocode | Yes | Algorithm 1 CGPFL: Personalized Federated Learning with Contextualized Generalization |
| Open Source Code | No | The paper does not provide any links to open-source code or explicitly state that code for the methodology is available. |
| Open Datasets | Yes | Three datasets including MNIST [Le Cun et al., 1998], CIFAR10 [Krizhevsky, 2009], and Fashion MNIST (FMNIST) [Xiao et al., 2017] are used in our experiments. |
| Dataset Splits | No | The paper specifies a train/test split ('75% are used for training and the remaining 25% for testing') but does not mention a separate validation set. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., CPU, GPU models) used for running the experiments. |
| Software Dependencies | No | The paper mentions using a 'neural network (DNN)' and 'CNN' but does not specify any software libraries or their version numbers (e.g., TensorFlow, PyTorch, scikit-learn versions). |
| Experiment Setup | Yes | We set N = 40, α = 1, λ = 12, S = 5, lr = 0.005 and T = 200 for MNIST and Fashion MNIST (FMNIST), and T = 300, lr = 0.03 for CIFAR10, where lr denotes the learning rate. |