Federated MLOps: Secure CI/CD for Distributed Model Training and Deployment

Abstract

Federated Machine Learning (FL) has emerged as a promising approach for collaborative model training without sharing raw data, thereby preserving privacy. However, integrating FL with modern MLOps practices poses unique challenges in automating and securing the Continuous Integration and Continuous Deployment (CI/CD) pipelines. This paper proposes Federated MLOps, a framework that combines CI/CD principles with federated model training to enable secure, automated, and efficient deployment of distributed ML models. We describe the system architecture, security mechanisms, and pipeline orchestration strategies, and demonstrate the framework through a case study evaluating performance, scalability, and privacy preservation. Our results highlight the potential of Federated MLOps to enhance model reliability, reproducibility, and security in distributed learning environments.