Communication-Aware Collaborative Learning
Authors: Avrim Blum, Shelby Heinecke, Lev Reyzin6786-6793
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we study collaborative PAC learning with the goal of reducing communication cost at essentially no penalty to the sample complexity. We develop communication efficient collaborative PAC learning algorithms using distributed boosting. We then consider the communication cost of collaborative learning in the presence of classification noise. As an intermediate step, we show how collaborative PAC learning algorithms can be adapted to handle classification noise. With this insight, we develop communication efficient algorithms for collaborative PAC learning robust to classification noise. |
| Researcher Affiliation | Collaboration | Avrim Blum,1 Shelby Heinecke,2 Lev Reyzin3 1 Toyota Technological Institute at Chicago 2 Salesforce Research 3 University of Illinois at Chicago avrim@ttic.edu, shelby.heinecke@salesforce.com, lreyzin@uic.edu |
| Pseudocode | Yes | Algorithm 1: Personalized Learning (Blum et al. 2017) and Algorithm 2: Personalized Learning with Classification Noise are provided. |
| Open Source Code | No | The paper does not contain any statements about releasing code or links to source code repositories for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments with specific datasets; therefore, it does not provide information about public dataset availability for its own work. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical experiments or dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper does not mention any specific hardware used for running experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not include details on experimental setup, hyperparameters, or system-level training settings. |