Seung-hoo Hong, KangYoon Lee, Journal of Internet Computing and Services, Vol. 23, No. 3, pp. 97-110, Jun. 2022
10.7472/jksii.2022.23.3.97, Full Text:  HTML
Keywords: federated-learning, Framework, Open Source, effective development environment, MLOps

Abstract

Federated learning is a learning method capable of performing model learning without transmitting learning data. The IoT or healthcare field is sensitive to information leakage as it deals with users' personal information, so a lot of attention should be paid to system design, but when using federated-learning, data does not move from devices where data is collected. Accordingly, many federated-learning implementations have been developed, but detailed research on system design for the development and operation of systems using federated learning is insufficient. This study shows that measures for the life cycle, code version management, model serving, and device monitoring of federated learning are needed to be applied to actual projects and distributed to IoT devices, and we propose a design for a development environment that complements these points. The system proposed in this paper considered uninterrupted model-serving and includes source code and model version management, device state monitoring, and server-client learning schedule management.

Statistics
Cite this article