Gradient Driven Rewards to Guarantee Fairness in Collaborative Machine Learning
Authors: Xinyi Xu, Lingjuan Lyu, Xingjun Ma, Chenglin Miao, Chuan Sheng Foo, Bryan Kian Hsiang Low
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically demonstrate the effectiveness of our fair gradient reward mechanism on multiple benchmark datasets in terms of fairness, predictive performance, and time overhead. |
| Researcher Affiliation | Collaboration | Department of Computer Science, National University of Singapore, Republic of Singapore1 Sony AI2, School of Computer Science, Fudan University, People s Republic of China3 Department of Computer Science, University of Georgia, USA4 Institute for Infocomm Research, A*STAR, Republic of Singapore5 |
| Pseudocode | Yes | We provide the pseudocodes performed by the server and agent i N in each iteration t below. |
| Open Source Code | No | The paper does not provide any explicit statements about making the source code available or include a link to a code repository. |
| Open Datasets | Yes | We perform extensive experiments on image classification datasets like MNIST [26] and CIFAR-10 [21] and text classification datasets like movie review (MR) [44] and Stanford sentiment treebank (SST) [20]. |
| Dataset Splits | Yes | We perform extensive experiments on image classification datasets like MNIST [26] and CIFAR-10 [21] and text classification datasets like movie review (MR) [44] and Stanford sentiment treebank (SST) [20]. We use a 2-layer convolutional neural network (CNN) for MNIST [25], a 3-layer CNN for CIFAR-10 [22], and a text embedding CNN for MR and SST [20]. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments, such as GPU models, CPU specifications, or memory. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | Hyperparameters. We find that α [0.8, 1)(i.e., relative weight on ri,t 1 in (4)), β [1, 2] (i.e., degree of altruism in (5)) and Γ [0.1, 1] (i.e., normalization coefficient in (1)) are effective in achieving competitive predictive performance and fairness. In our experiments, we set α = 0.95, β = [1, 1.2, 1.5, 2], and Γ = 0.5 for MNIST, Γ = 0.15 for CIFAR-10, and Γ = 1 for SST and MR. |