Federated Machine Learning for Collaborative DevOps in Multi-Tenant Cloud Environments

Abstract

Federated Machine Learning (FML) represents a transformative approach to collaborative DevOps, particularly within multi-tenant cloud environments. By enabling decentralized machine learning model training across various data sources without necessitating data centralization, FML enhances data privacy, security, and compliance, crucial aspects for multi-tenant cloud platforms. This research delves into the integration of FML within DevOps practices, highlighting its potential to address key challenges such as data security, model accuracy, and operational efficiency. Key findings from the study demonstrate that FML can significantly improve collaborative model training processes, enhance predictive maintenance, and streamline automated remediation in cloud environments. The research also outlines a robust framework for implementing FML in DevOps pipelines, backed by case studies and performance evaluations from real-world applications in financial services and healthcare sectors. By exploring advanced strategies and presenting practical insights, this study contributes valuable knowledge to the fields of federated learning, DevOps, and cloud computing, paving the way for more resilient and efficient cloud-based operations.