Incentivized Communication for Federated Bandits
Authors: Zhepei Wei, Chuanhao Li, Haifeng Xu, Hongning Wang
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive empirical experiments on both synthetic and real-world datasets further validate the effectiveness of the proposed method across various environments. |
| Researcher Affiliation | Academia | University of Virginia1 University of Chicago2 |
| Pseudocode | Yes | Algorithm 1 INC-FEDUCB Algorithm Algorithm 2 Payment-free Incentive Mechanism Algorithm 3 Payment-efficient Incentive Mechanism Algorithm 4 Heuristic Search |
| Open Source Code | No | The paper does not provide an explicit statement about open-sourcing the code or a link to a code repository. |
| Open Datasets | Yes | We also conduct comprehensive experiments on the real-world recommendation dataset Movie Lens [12]. |
| Dataset Splits | No | The paper does not explicitly provide training/test/validation dataset splits. It mentions using "synthetic and real-world datasets" but does not detail how these datasets were split for different phases of model development or evaluation. |
| Hardware Specification | No | The paper states: "We simulate the incentivized federated bandit problem under various environment settings." This implies a software simulation, and no specific hardware (like GPU/CPU models or cloud instances) is mentioned for running these simulations. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software, libraries, or programming languages used in the experiments. |
| Experiment Setup | Yes | Specifically, we create an environment of N = 50 clients with cost of data sharing Dp = {Dp 1, , Dp N}, total number of iterations T = 5, 000, feature dimension d = 25, and time-varing arm pool size K = 25. By default, we set Dp i = Dp R, i [N]. Specifically, we explored different hyper-parameter settings for INC-FEDUCB with determinant ratio threshold β [0.3, 0.7, 1], and various environmental configurations with data sharing cost Dp [1, 10, 100]. |