earticle

한국어

Federated Learning (FL) facilitates the design and training of deep learning (DL) models across multiple institutions and departments. For users with limited private data, FL can enhance the performance of local models by leveraging collective data from various sources. However, the devices participating in FL often differ in terms of network conditions, hardware and power capabilities. This results in heterogeneity across the computing, storage, and communication capabilities of the devices involved. Consequently, FL faces significant challenges due to device heterogeneity. Ignoring the inherent differences among clients when implementing FL protocols can significantly hinder both the efficiency and effectiveness of the training process. With the wide adaptability of FL for training DL models in resourceconstraint environments, the challenges caused by device heterogeneity have become increasingly apparent, highlighting the need for innovative strategies to minimize its impact on model aggregation. Therefore, this paper aims to review some of the most practical and effective existing proposed solutions. Future research can benefit from these solutions, as they can reduce the impact of device heterogeneity in FL.