Federated Linear Bandits with Finite Adversarial Actions
Authors: Li Fan, Ruida Zhou, Chao Tian, Cong Shen
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiment results corroborate the theoretical analysis and demonstrate the effectiveness of Fed Sup Lin UCB on both synthetic and realworld datasets. |
| Researcher Affiliation | Academia | Li Fan University of Virginia lf2by@virginia.edu Ruida Zhou Texas A&M University ruida@tamu.edu Chao Tian Texas A&M University chao.tian@tamu.edu Cong Shen University of Virginia cong@virginia.edu |
| Pseudocode | Yes | Algorithm 1 Sync(s, server, client 1, . . . client n); Algorithm 2 S-LUCB; Algorithm 3 Async-Fed Sup Lin UCB; Algorithm 4 Sync-Fed Sup Lin UCB |
| Open Source Code | No | The paper does not include any explicit statement or link indicating the release of open-source code for the described methodology. |
| Open Datasets | Yes | We have carried out experiments utilizing the real-world recommendation dataset Movie Lens 20M (Harper and Konstan, 2015). |
| Dataset Splits | No | The paper mentions using datasets but does not provide specific training/validation/test split percentages, sample counts, or explicit instructions for dataset partitioning. |
| Hardware Specification | No | The paper describes experiment results using synthetic and real-world datasets but does not provide specific hardware details (e.g., CPU/GPU models, memory, or cloud instance types) used for running these experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper mentions parameters like T, M, d, A for synthetic data and TF-IDF feature d=25, K=20 for real-world data, but does not provide specific hyperparameters (e.g., learning rate, batch size, optimizer settings) or detailed system-level training configurations. |