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Federated Learning (FL) offers a solution to the challenges of traditional centralized machine learning by enabling decentralized training and exchanging only model updates instead of raw data. This approach addresses key issues such as privacy concerns and high data transfer costs. However, integrating FL into existing Machine Learning Operations (MLOps) pipelines presents challenges, particularly regarding model versioning, synchronization, and scalability. This paper introduces a concept for centralized model management that enables the integration of FL into existing MLOps pipelines without the need to overhaul the existing architecture. The concept is specifically developed for deployment in an industrial setting, with plans for implementing both FL and Transfer Learning (TL) in the future. The proposed approach emphasizes flexibility, ensuring that it can be easily extended to accommodate additional methods and seamlessly integrated into diverse, pre-existing infrastructure. The management of the system is facilitated using the open-source tool MLflow, which offers significant advantages over specialized FL frameworks, particularly in terms of adaptability and resource optimization.

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Efficient Federated Learning Integration into Existing MLOps Pipelines via Centralized Model Management

  • 会议论文

  • 发表日期：

    2026年

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  • Tatjana Krau,
  • Florian Huber,
  • Teena Chirakal,
  • Tobias Ricken,
  • Bernd Lüdemann-Ravit,
  • Frieder Heieck

摘要

Federated Learning (FL) offers a solution to the challenges of traditional centralized machine learning by enabling decentralized training and exchanging only model updates instead of raw data. This approach addresses key issues such as privacy concerns and high data transfer costs. However, integrating FL into existing Machine Learning Operations (MLOps) pipelines presents challenges, particularly regarding model versioning, synchronization, and scalability. This paper introduces a concept for centralized model management that enables the integration of FL into existing MLOps pipelines without the need to overhaul the existing architecture. The concept is specifically developed for deployment in an industrial setting, with plans for implementing both FL and Transfer Learning (TL) in the future. The proposed approach emphasizes flexibility, ensuring that it can be easily extended to accommodate additional methods and seamlessly integrated into diverse, pre-existing infrastructure. The management of the system is facilitated using the open-source tool MLflow, which offers significant advantages over specialized FL frameworks, particularly in terms of adaptability and resource optimization.