• Journal of Internet Computing and Services
• /
• v.24 no.4
• /
• pp.1-13
• /
• 2023

As federated learning brings a large paradigm to modern artificial intelligence research, efforts are being made to incorporate federated learning into research in various fields. However, researchers who apply federated learning face the problem of choosing a federated learning framework and benchmark tool suitable for their situation and purpose. This study aims to present guidelines for selection of federated learning frameworks and benchmark tools considering the circumstances of researchers who apply federated learning in practice. In particular, there are three main contributions in this study. First, it generalizes the situation of the researcher applying federated learning by combining it with the goal of federated learning and proposes guidelines for selecting a federated learning framework suitable for each situation. Second, it shows the suitability of selection by comparing the characteristics and performance of each federated learning framework to the researcher. Finally, the limitations of the existing federated learning framework and a plan for real-world federated learning operation are proposed.

• Journal of Platform Technology
• /
• v.11 no.4
• /
• pp.80-88
• /
• 2023

To construct a learning model in the field of artificial intelligence, a dataset should be collected and be delivered to the central server where the learning model is constructed. Federated learning is a machine learning method building a global learning model without transmitting data located in a client side in a collaborative manner. It can be used to protect privacy, and after constructing a local trained model on individual clients, the parameters of the local model are aggregated centrally to update the global model. In this paper, we reuse the existing learning parameter to improve federated learning, describe incremental federated learning. For this work, we do experiments using the federated learning framework named Flower, and evaluate the experiment results with regard to elapsed time and precision when executing optimization algorithms.

• Journal of Internet Computing and Services
• /
• v.23 no.3
• /
• pp.97-110
• /
• 2022

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.