On Differentially Private Federated Linear Contextual Bandits
Authors: Xingyu Zhou, Sayak Ray Chowdhury
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we support our theoretical results with numerical evaluations over contextual bandit instances generated from both synthetic and real-life data. and 6 SIMULATION RESULTS AND CONCLUSIONS We evaluate regret performance of Algorithm 1 under silo-level LDP and SDP, which we abbreviate as LDP-Fed Lin UCB and SDP-Fed Lin UCB, respectively. |
| Researcher Affiliation | Collaboration | Xingyu Zhou Wayne State University, USA Email: xingyu.zhou@wayne.edu Sayak Ray Chowdhury Microsoft Research, India Email: t-sayakr@microsoft.com |
| Pseudocode | Yes | Algorithm 1 Private-Fed Lin UCB |
| Open Source Code | No | The paper does not explicitly state that the code for the described methodology is open-source or provide a link to a code repository. |
| Open Datasets | Yes | We generate bandit instances from Microsoft Learning to Rank dataset (Qin & Liu, 2013). |
| Dataset Splits | No | The paper discusses the use of synthetic and real-life data for evaluation but does not specify explicit training/validation/test dataset splits (e.g., percentages, sample counts, or predefined splits). |
| Hardware Specification | No | The paper does not specify any particular hardware components (e.g., GPU/CPU models, memory, or cloud instances) used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., programming languages, libraries, or frameworks) used for implementation or experimentation. |
| Experiment Setup | Yes | We fix confidence level α=0.01, batchsize B = 25 and study comparative performances under varying privacy budgets ε, δ. |