Federated Online and Bandit Convex Optimization
Authors: Kumar Kshitij Patel, Lingxiao Wang, Aadirupa Saha, Nathan Srebro
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our work is the first attempt towards a systematic understanding of federated online optimization with limited feedback, and it attains tight regret bounds in the intermittent communication setting for both first and zeroth-order feedback. Our results thus bridge the gap between stochastic and adaptive settings in federated online optimization. |
| Researcher Affiliation | Collaboration | Kumar Kshitij Patel 1 Lingxiao Wang 1 Aadirupa Saha 2 Nati Srebro 1 [...] 1TTIC 2Apple. |
| Pseudocode | Yes | Algorithm 1: Non-collaborative OGD (η) [...] Algorithm 2: FEDPOSGD (η, δ) with one-point bandit feedback [...] Algorithm 3: FEDOSGD (η, δ) with two-point bandit feedback |
| Open Source Code | No | The paper does not contain any statement about releasing open-source code for the described methods, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments on datasets. It discusses 'function classes' such as FG,B and FH,B, which are mathematical definitions, not actual datasets. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any computational experiments or hardware specifications used for running them. |
| Software Dependencies | No | The paper is theoretical and does not mention any software implementations or dependencies with version numbers. |
| Experiment Setup | No | The paper describes theoretical choices for algorithm parameters (e.g., 'If we choose η = B G Kd1/4 o , and δ = Bd1/4 '), but these are part of the mathematical analysis and proofs, not specific hyperparameter values or training settings for an empirical experimental setup. |