no code implementations • 24 May 2022 • Hideya Ochiai, Yuwei Sun, Qingzhe Jin, Nattanon Wongwiwatchai, Hiroshi Esaki
Federated learning has allowed training of a global model by aggregating local models trained on local nodes.
no code implementations • 21 Oct 2021 • Yichuan Chen, Manabu Tsukada, Hiroshi Esaki
The experimental results indicate that the proposed system outperforms seven out of ten test scenes in obtaining lower depth observation error.
no code implementations • 30 Jul 2021 • Yuwei Sun, Hideya Ochiai, Hiroshi Esaki
Wider coverage and a better solution to a latency reduction in 5G necessitate its combination with multi-access edge computing (MEC) technology.
1 code implementation • IEEE Open Journal of the Communications Society (Conference version: IJCNN) 2020 • Yuwei Sun, Hiroshi Esaki, Hideya Ochiai.
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.
1 code implementation • International Joint Conference on Neural Networks (IJCNN) 2020 • Yuwei Sun, Hideya Ochiai, Hiroshi Esaki
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.
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