Redefining Contributions: Shapley-Driven Federated Learning
Authors: Nurbek Tastan, Samar Fares, Toluwani Aremu, Samuel Horváth, Karthik Nandakumar
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on CIFAR-10, Chest X-Ray, and Fed-ISIC2019 datasets demonstrate the effectiveness of our approach in improving utility, efficiency, and fairness in FL systems. |
| Researcher Affiliation | Academia | Nurbek Tastan , Samar Fares , Toluwani Aremu , Samuel Horvath , Karthik Nandakumar Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi, UAE {nurbek.tastan, samar.fares, toluwani.aremu, samuel.horvath, karthik.nandakumar}@mbzuai.ac.ae |
| Pseudocode | Yes | Algorithm 1 Shap Fed algorithm |
| Open Source Code | Yes | The code can be found at https://github.com/tnurbek/shapfed. |
| Open Datasets | Yes | CIFAR-10 [Krizhevsky et al., 2009]: This dataset comprises 60,000 RGB images, each with dimensions of 32 × 32 pixels, spanning 10 different classes. It is divided into a training set of 50,000 images and a testing set of 10,000 images. Chest X-Ray [Rahman et al., 2020]: The Tuberculosis (TB) Chest X-ray Database is a comprehensive collection of chest X-ray images containing 700 publicly accessible TB-positive images and 3500 normal images. Fed-ISIC2019 [Ogier du Terrail et al., 2022]: This dataset is an amalgamation of the ISIC 2019 challenge dataset and the HAM1000 database, presenting a total of 23,247 dermatological images of skin lesions (8 classes). |
| Dataset Splits | No | While CIFAR-10's train/test split is mentioned, explicit and detailed validation splits or methodologies for creating them are not provided for all datasets (e.g., Chest X-Ray, Fed-ISIC2019) in the main text for full reproducibility. The mention of an "auxiliary validation set" is conceptual, not a description of its use in their experimental setup details. |
| Hardware Specification | No | The paper mentions models like Res Net-34 and Efficient Net B0, but it does not provide specific hardware details such as GPU models, CPU models, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions using SGD optimizer and specific model architectures (Res Net-34, Efficient Net B0) but does not provide specific version numbers for any software libraries, frameworks (e.g., PyTorch, TensorFlow), or programming languages used. |
| Experiment Setup | Yes | CIFAR-10. We leverage the Res Net-34 architecture trained using the SGD optimizer with a fixed learning rate of 0.01. For FL, we use 50 communication rounds. Chest X-Ray. ... We use SGD optimizer with a learning rate of 0.01, momentum of 0.9, and weight decay of 5 × 10−4. The models are trained for 50 rounds. Fed-ISIC2019. We employ the Efficient Net B0 model and use the same training settings as in CIFAR-10 with 200 communication rounds. |