Core Concepts
CDML systems offer unique design options for collaborative machine learning, with key traits that impact system performance and functionality.
Abstract
The content introduces the concept of Collaborative Distributed Machine Learning (CDML) systems, focusing on their initialization, operation, and dissolution phases. It discusses roles like configurator, coordinator, selector, trainer, and updater in CDML systems. The article also delves into the design options available for customizing CDML systems to meet specific requirements.
Initialization Phase:
Agents form a coalition under a configurator agent.
Roles include configurator, coordinator, selector, trainer, and updater.
Configurator defines ML model specifications and registers the coalition.
Agents apply for roles based on CDML system specifications.
Operation Phase:
Selector agent chooses trainer and updater agents.
Trainer agents compute interim results based on local data.
Updater agents use interim results to update ML models.
Dissolution Phase:
Agents stop executing processes as collaboration ends.
Stats
Inadequate training data can lead to large generalization errors in ML models [1].
Strict data protection laws hinder access to sufficient training data [6].
Federated learning aims to preserve training data confidentiality [10].
Quotes
"In CDML systems, trainer agents receive ML tasks from other agents and use local training data to accomplish ML tasks."
"Agents only share locally computed training results with other agents in CDML systems."