Federated Neuro-Symbolic Learning
Authors: Pengwei Xing, Songtao Lu, Han Yu
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments based on both synthetic and real-world data demonstrate significant advantages of Fed NSL compared to five state-of-the-art methods. It outperforms the best baseline by 17% and 29% in terms of unbalanced average training accuracy and unseen average testing accuracy, respectively. |
| Researcher Affiliation | Collaboration | 1College of Computing and Data Science, Nanyang Technological University, Singapore 2IBM Thomas J. Watson Research Center Yorktown Heights, USA. Correspondence to: Pengwei Xing <pengwei001@e.ntu.edu.sg>, Songtao Lu <songtao@ibm.com>, Han Yu <han.yu@ntu.edu.sg>. |
| Pseudocode | Yes | Algorithm 1 Fed NSL |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a direct link to a code repository. |
| Open Datasets | Yes | For the real-data experiment, we utilize a document-level DWIE (Document-Level Web Information Extraction) dataset (Zaporojets et al., 2021) that has been pre-processed following the methods outlined in (Ru et al., 2021). |
| Dataset Splits | No | The paper specifies training and testing splits ('within each client, the documents are further divided into training and testing subsets, maintaining a 3 : 1 ratio') but does not explicitly mention a separate validation split or how it's handled. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware used (e.g., CPU, GPU models) to run its experiments. |
| Software Dependencies | No | The paper mentions 'a transformer model' and 'neural network classifiers' but does not specify any software dependencies with version numbers (e.g., PyTorch 1.x, Python 3.x, CUDA). |
| Experiment Setup | Yes | For numeric experiment, we use an integrated model setup combining deep learning classifiers with a GMM tailored for a federated learning context. The setup features two neural network classifiers, each with an input dimension of 2 to accommodate the two-dimensional features of our synthetic dataset, a hidden layer comprising 64 units to capture complex data patterns without overfitting, and an output layer with 3 units equipped with a softmax function for 3-class classification. Parallelly, the GMM is configured with 3 components to correspond with the dataset s 3 classes, where the means are initialized based on preliminary data analysis or classifier outputs, and covariance matrices are set to reflect initial data variance, facilitating adaptive learning through the EM processing. |