Federated learning has allowed training of a global model by aggregating local models trained on local nodes.
The experimental results indicate that the proposed system outperforms seven out of ten test scenes in obtaining lower depth observation error.
Wider coverage and a better solution to a latency reduction in 5G necessitate its combination with multi-access edge computing (MEC) technology.
We propose Segmented-Federated Learning (Segmented-FL), where by employing periodic local model evaluation and network segmentation, we aim to bring similar network environments to the same group.
In this research, a segmented federated learning is proposed, different from a collaborative learning based on single global model in a traditional federated learning model, it keeps multiple global models which allow each segment of participants to conduct collaborative learning separately and rearranges the segmentation of participants dynamically as well.
Ranked #1 on Network Intrusion Detection on SIDD-Image