Communication Efficient Federated Learning for Generalized Linear Bandits
Authors: Chuanhao Li, Hongning Wang
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We rigorously proved, though the setting is more general and challenging, our algorithm can attain sub-linear rate in both regret and communication cost, which is also validated by our extensive empirical evaluations. ... Extensive empirical evaluations on both synthetic and real-world datasets are performed to validate the effectiveness of our algorithm. |
| Researcher Affiliation | Academia | 1Department of Computer Science, University of Virginia |
| Pseudocode | Yes | Algorithm 1 Fed GLB-UCB; Algorithm 2 ONS-Update; Algorithm 3 AGD-Update |
| Open Source Code | Yes | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] |
| Open Datasets | Yes | Real-world Dataset ... Cover Type, Magic Telescope and Mushroom from the UCI Machine Learning Repository [5]. [5] Dheeru Dua and Casey Graff. UCI machine learning repository, 2017. |
| Dataset Splits | No | The paper describes parameters for the synthetic and real-world datasets (e.g., T=2000, N=200), and how data is generated/collected online in a bandit setting, but it does not specify explicit train/validation/test dataset splits with percentages or counts, which are typical for supervised learning reproducibility. |
| Hardware Specification | No | Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [No] |
| Software Dependencies | No | The paper does not provide specific names and version numbers of software dependencies (e.g., programming languages, libraries, frameworks, or solvers) used for the experiments. |
| Experiment Setup | Yes | We simulated the federated GLB setting defined in Section 3.3, with T = 2000, N = 200, d = 10, S = 1, At (K = 25) uniformly sampled from a ℓ2 unit sphere, and reward yt,,i Bernoulli(µ(x t,,iθ )), with µ(z) = (1 + exp( z)) 1. ... we pre-processed these datasets following the steps in prior works [8], with T = 2000 and N = 20. ... we run Fed GLB-UCB with different threshold value D (logarithmically spaced between 10 1 and 103) and its variants with different number of global updates B. |