Practical One-Shot Federated Learning for Cross-Silo Setting
Authors: Qinbin Li, Bingsheng He, Dawn Song
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments on various tasks show that Fed KT can significantly outperform the other state-of-the-art federated learning algorithms with a single communication round. |
| Researcher Affiliation | Academia | Qinbin Li1 , Bingsheng He1 , Dawn Song2 1National University of Singapore 2University of California, Berkeley {qinbin, hebs}@comp.nus.edu.sg, dawnsong@cs.berkeley.edu |
| Pseudocode | Yes | Algorithm 1: The Fed KT algorithm |
| Open Source Code | Yes | The code is publicly available 2. 2https://github.com/Qinbin Li/Fed KT |
| Open Datasets | Yes | To evaluate Fed KT, we conduct experiments on four public datasets: (1) A random forest on Adult dataset. (2) A gradient boosting decision tree (GBDT) model on cod-rna dataset. (3) A multilayer perceptron (MLP) with two hidden layers on MNIST dataset. (4) A CNN on extended SVHN dataset. |
| Dataset Splits | No | For the first two datasets, we split the original dataset at random into train/test/public sets with a 75%/12.5%/12.5% proportion. For MNIST and SVHN, we use one half of the original test dataset as the public dataset and the remaining as the final test dataset. The paper mentions a 'public' dataset which is used in the training process of the student and final models via knowledge transfer, but not a distinct 'validation' set for hyperparameter tuning in the traditional sense. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not explicitly state specific software dependencies with version numbers. |
| Experiment Setup | Yes | The number of trees is set to 100 and the maximum tree depth is set to 6. (2) A gradient boosting decision tree (GBDT) model on cod-rna dataset. The maximum tree depth is set to 6. (3) A multilayer perceptron (MLP) with two hidden layers on MNIST dataset. Each hidden layer has 100 units using Re Lu activations. (4) A CNN on extended SVHN dataset. The CNN has two 5x5 convolution layers followed with 2x2 max pooling (the first with 6 channels and the second with 16 channels), two fully connected layers with Re Lu activation (the first with 120 units and the second with 84 units), and a final softmax output layer. By default, we set the number of parties to 50 for Adult and cod-rna and to 10 for MNIST and SVHN. We set s to 2 and t to 5 by default for all datasets. |