Federated Multi-Armed Bandits
Authors: Chengshuai Shi, Cong Shen9603-9611
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments using both synthetic and real-world datasets corroborate the theoretical analysis and provide interesting insight into the proposed algorithms. Numerical simulations on synthetic and real-world datasets demonstrate the effectiveness and efficiency of the proposed algorithms and offer some interesting insights. |
| Researcher Affiliation | Academia | Department of Electrical and Computer Engineering, University of Virginia |
| Pseudocode | Yes | Algorithm 1 Fed2-UCB: client m; Algorithm 2 Fed2-UCB: central server |
| Open Source Code | No | The paper does not provide an explicit statement or a link to open-source code for the described methodology. |
| Open Datasets | Yes | The Movie Lens dataset (Cantador, Brusilovsky, and Kuflik 2011) is used for the real-world evaluation as an implementation of recommender system, which has been widely adopted in MAB studies (Oh and Iyengar 2019; Mahadik et al. 2020). |
| Dataset Splits | No | The paper describes the datasets used (synthetic and Movie Lens) but does not provide specific training, validation, and test dataset splits or a methodology for generating them. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers needed to replicate the experiments. |
| Experiment Setup | Yes | The communication cost is set to be C = 1. A bandit game with K = 10 arms is used to mimic 10 candidate channels, and Gaussian distributions with σ = 0.5 are used to generate local observations of the channel availability. The means of global arms are in the interval [0.7, 0.8] with = 0.02. ... M = 5 clients are involved ... with f(p) = 10 log(T) ... For the approximate model, the same set of global arms is used while the local models are generated by Gaussian distributions with σc = 0.02. Fig. 5 shows that Fed2-UCB with f(p) = 100 and g(p) = 2p ... Under a reduced time horizon T = 104, Fig. 6 provides a finer look at the shape of regret curves of Fed2-UCB ... With a short update period f(p) = 10 ... f(p) = 50 ... The suboptimality gap of the pre-processed data is 0.0053. Using Fed2-UCB and f(p) = 200 with g(p) = 2p... |