Federated Causality Learning with Explainable Adaptive Optimization
Authors: Dezhi Yang, Xintong He, Jun Wang, Guoxian Yu, Carlotta Domeniconi, Jinglin Zhang
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on synthetic and real datasets show that Fed Causal can effectively deal with non-independently and identically distributed (non-IID) data and has a superior performance. |
| Researcher Affiliation | Academia | Dezhi Yang1, 2, Xintong He3, Jun Wang2*, Guoxian Yu1, 2, Carlotta Domeniconi4, Jinglin Zhang5 1School of Software, Shandong University, Jinan, China 2SDU-NTU Joint Centre for AI Research, Shandong University, Jinan, China 3Department of Mathematics, National University of Singapore, Singapore 4Departiment of Computer Science, George Mason University, VA, USA 5School of Control Science and Engineering, Shandong University, Jinan, China dzyang@mail.sdu.edu.cn, E0966399@u.nus.edu.sg, {kingjun, gxyu,jinglin.zhang}@sdu.edu.cn, carlotta@cs.gmu.edu |
| Pseudocode | Yes | Algorithm 1: Fed Causal: Federated Causal Learning |
| Open Source Code | Yes | The code of Fed Causal is shared at https://www.sdu-idea.cn/pubDetail?pubId=279. |
| Open Datasets | Yes | We evaluate Fed Causal on a protein signaling network based on expression levels of proteins and phospholipid given by (Sachs et al. 2005), which is generally accepted by the biology community. |
| Dataset Splits | No | The paper describes generating data or distributing samples to clients (e.g., 'generate 200 samples for each client', 'randomly select n = 7460 samples and evenly distribute them to 10 clients'), but it does not specify explicit training, validation, and test dataset splits (e.g., percentages or counts for each). |
| Hardware Specification | Yes | We use the same server (Ubuntu 18.04.5, Intel Xeon Gold 6248R and Nvidia RTX 3090) to perform experiments and report the structural hamming distance (SHD), true positive rate (TPR) and false discovery rate (FDR) of the estimated DAGs, averaged over 10 random runs. |
| Software Dependencies | Yes | We use the same server (Ubuntu 18.04.5, Intel Xeon Gold 6248R and Nvidia RTX 3090) to perform experiments and report the structural hamming distance (SHD), true positive rate (TPR) and false discovery rate (FDR) of the estimated DAGs, averaged over 10 random runs. |
| Experiment Setup | No | We provide the hyperparameter settings of Fed Causal and other baselines in the supplementary file (Yang et al. 2023). |