Differentially Private Federated Bayesian Optimization with Distributed Exploration
Authors: Zhongxiang Dai, Bryan Kian Hsiang Low, Patrick Jaillet
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also use real-world experiments to show that DP-FTS-DE achieves high utility (competitive performance) with a strong privacy guarantee (small privacy loss) and induces a trade-off between privacy and utility. [...] Next, we empirically demonstrate that DP-FTS-DE delivers an effective performance with a strong privacy guarantee and induces a favorable trade-off between privacy and utility in real-world applications (Sec. 5). [...] 5 Experiments |
| Researcher Affiliation | Academia | Dept. of Computer Science, National University of Singapore, Republic of Singapore Dept. of Electrical Engineering and Computer Science, MIT, USA |
| Pseudocode | Yes | Algorithm 1 DP-FTS-DE (central server) [...] Algorithm 2 BO-Agent-An(t, ωjoint t 1 = (ω(i) t 1)i [P ]) |
| Open Source Code | Yes | Our code is here: https://github.com/daizhongxiang/ Differentially-Private-Federated-Bayesian-Optimization |
| Open Datasets | Yes | We adopt 3 commonly used datasets in FL and FBO [12, 60]. We firstly use a landmine detection dataset with N = 29 landmine fields [66] and tune 2 hyperparameters of SVM for landmine detection. Next, we use data collected using mobile phone sensors when N = 30 subjects are performing 6 activities [2] and tune 3 hyperparameters of logistic regression for activity classification. Lastly, we use the images of handwritten characters by N = 50 persons from EMNIST (a commonly used benchmark in FL) [8] and tune 3 hyperparameters of a convolutional neural network used for image classification. |
| Dataset Splits | No | The paper discusses the use of datasets but does not explicitly provide details about train/validation/test splits, percentages, or methodologies for creating these splits. It mentions 'validation error' in figures but not how the validation set was formed. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running the experiments. It only mentions 'growing computational capability of edge devices' in the introduction, which is not an experimental setup detail. |
| Software Dependencies | No | The paper does not explicitly list software dependencies with version numbers. |
| Experiment Setup | Yes | In all 3 experiments, we choose P = 4, S = 22.0, M = 100, and 1 pt = 1/t. [...] For example, in the synthetic experiments, we set N = 200, Ninit = 10, M = 50, 1 pt = 1/t. In the landmine detection experiment, N = 29, Ninit = 10, P = 4, S = 22, M = 100, 1 pt = 1/t. For the human activity recognition, N = 30, Ninit = 10, P = 4, S = 22, M = 100, 1 pt = 1/t. For the EMNIST dataset, N = 50, Ninit = 10, P = 4, S = 22, M = 100, 1 pt = 1/t. |