Doubly Adversarial Federated Bandits
Authors: Jialin Yi, Milan Vojnovic
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide numerical evaluations of our algorithm to validate our theoretical results and demonstrate its effectiveness on synthetic and real-world datasets. |
| Researcher Affiliation | Academia | 1Department of Statistics, London School of Economics and Political Science, London, United Kingdom. |
| Pseudocode | Yes | Algorithm 1 FEDEXP3 |
| Open Source Code | Yes | The code for producing our experimental results is available online in an anonymous Github repository: https://github.com/jialinyi94/doubly-stochasticfederataed-bandit. |
| Open Datasets | Yes | We evaluate the two algorithms using the Movie Lens Latest-full dataset which contains 58,000 movies, classified into 20 genres, with 27,000,000 ratings (rating scores in {0.5, 1, . . . , 5}) from 280,000 users. |
| Dataset Splits | No | The paper describes the construction of synthetic datasets and the use of the Movie Lens Latest-full dataset, but does not specify explicit training, validation, or test dataset splits (e.g., percentages or sample counts for each split). |
| Hardware Specification | Yes | All the experiments are run on a desktop with AMD Ryzen 5 2600 Six-Core Processor and 16GB memory. |
| Software Dependencies | No | The paper mentions 'Python', 'Numpy package', and 'Network X package' but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | We set T = 3, 000 with N = 36 and K = 20. At each time step t, a subset Ut of N/2 agents are selected from V with replacement. For all activated agents Ut, the loss for arm i is sampled independently from Bernoulli distribution with mean µi = (i − 1)/(K − 1). |