Fed-NILM: A Federated Learning-based Non-Intrusive Load Monitoring Method for Privacy-Protection

no code implementations • 24 May 2021 • Haijin Wang, Caomingzhe Si, Junhua Zhao, Guolong Liu, Fushuan Wen

However, inadequate load data and the risk of power consumer privacy breaches may be encountered by local data owners during the NILM model training.

Federated Learning Non-Intrusive Load Monitoring

A Federated Learning Framework for Non-Intrusive Load Monitoring

no code implementations • 4 Apr 2021 • Haijin Wang, Caomingzhe Si, Junhua Zhao

The global model is generated by weighted averaging the locally-trained model weights to gather the locally-trained model information.

Federated Learning Model Selection +1

Rethinking Convolutional Features in Correlation Filter Based Tracking

no code implementations • 30 Dec 2019 • Fang Liang, Wenjun Peng, Qinghao Liu, Haijin Wang

In this paper, we revisit a hierarchical deep feature-based visual tracker and found that both the performance and efficiency of the deep tracker are limited by the poor feature quality.

feature selection Visual Object Tracking