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In this paper, we propose Meta-HAR, a federated representation learning framework, in which a signal embedding network is meta-learned in a federated manner, while the learned signal representations are ... Human activity recognition (HAR) based on mobile sensors plays an important role in ubiquitous computing. ... To solve these challenges, in this paper we propose Meta-HAR, a federated representation learning framework for human activity recognition, where a shared, global deep embedding network is meta-trained ...doi:10.1145/3442381.3450006 arXiv:2106.00615v1 fatcat:rqevcqrcx5g53jdrqer7rk3u34
Federated learning (FL) is an emerging promising privacy-preserving machine learning paradigm and has raised more and more attention from researchers and developers. ... The findings have evidenced that SL is supposed to be suitable for most application scenarios, no matter whether the dataset is balanced, polluted, or biased over irrelevant features. ... Meta-HAR: Federated Representation Learning for Human Activity Recognition. ...arXiv:2201.05286v2 fatcat:af5f6lf7hvgsjpeed6cfczwqsq