| Popis: |
Federated Learning has witnessed a rapid growth in research and industry applications as it offers the benefits of privacy preserving while contributing to the global model training. Cross-silo federated learning systems which are usually geographically distributed and cross-organizational are becoming a reality. Although DevOps and MLOps methodologies may help improving traditional machine learning systems' development efficiency and productivity, it is still challenging for them to develop cross-silo federated learning systems in a productive way. In this paper, we propose FLOps (Federated Learning Operations), a new methodology for developing cross-silo federated learning systems efficiently and continuously. By elaborating the challenges that FLOps is facing, we construct the lifecycle of FLOps, and propose approaches to FLOps. Finally, we highlight potential research directions of FLOps. |
