DevOps Pipelines for Federated Learning: Implementing MLOps in Decozntracized Machine Learning Systems
Keywords:
DevOps, federated learning, MLOpsAbstract
This paper explores the adaptation of DevOps pipelines for federated learning environments, focusing on the unique challenges of implementing MLOps in decentralized machine learning systems. Federated learning allows for training machine learning models across multiple decentralized devices or servers without the need to share raw data. However, implementing MLOps practices in such settings presents a set of challenges distinct from traditional centralized machine learning systems. The paper discusses the fundamental principles of DevOps and MLOps, reviews the specific needs of federated learning, and suggests methodologies for the effective deployment of MLOps within these frameworks. Key considerations include version control, continuous integration, deployment strategies, and monitoring frameworks tailored for decentralized systems. The findings aim to provide a structured approach for organizations seeking to leverage federated learning while maintaining robust operational practices.
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