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A Privacy and Energy-Aware Federated Framework for Human Activity Recognition.
Sensors (Basel). 2023 Nov 22;23(23):9339. doi: 10.3390/s23239339.
Sensors (Basel). 2023.
PMID: 38067712
Free PMC article.
This paper proposes a federated learning framework integrating spiking neural networks (SNNs) with long short-term memory (LSTM) networks for energy-efficient and privacy-preserving HAR. ...
This paper proposes a federated learning framework integrating spiking neural networks (SNNs) with long short-term memory (LSTM) netw …
FedBranched: Leveraging Federated Learning for Anomaly-Aware Load Forecasting in Energy Networks.
Manzoor HU, Khan AR, Flynn D, Alam MM, Akram M, Imran MA, Zoha A.
Manzoor HU, et al.
Sensors (Basel). 2023 Mar 29;23(7):3570. doi: 10.3390/s23073570.
Sensors (Basel). 2023.
PMID: 37050631
Free PMC article.
The proposed framework was implemented on substation-level energy data with nine clients for short-term load forecasting using an artificial neural network (ANN). FedBranched took two clustering rounds and resulted in two different branches having individual global models. …
The proposed framework was implemented on substation-level energy data with nine clients for short-term load forecasting using an art …
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