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Federated Learning (FL) enables collaborative model training while ensuring data privacy, a crucial requirement for many organizations. Open-Cluster-Management (OCM) extends this capability by managing both public and private clusters, making it an ideal solution for environments with strict data governance, such as private clouds. Instead of transferring sensitive data between clusters, OCM leverages FL to move models, significantly reducing bandwidth usage and minimizing the need for large-scale data storage within individual clusters.
Furthermore, OCM standardizes FL workflows, providing seamless integration with platforms like Flower, OpenFL, and FATE through a unified interface. This session will demonstrate how OCM enhances FL with scalable multi-cluster management, cost-efficient operations, and standardized workflows to enable the development of smarter, privacy-focused AI solutions.
