Camouflage learning

Loading...
Thumbnail Image
Access rights
openAccess
Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Date
2021-06-24
Major/Subject
Mcode
Degree programme
Language
en
Pages
17
Series
IEEE Transactions on Mobile Computing
Abstract
Ambient intelligence demands collaboration schemes for distributed constrained devices which are not only highly energy efficient in distributed sensing, processing and communication, but which also respect data privacy. Traditional algorithms for distributed processing suffer in Ambient intelligence domains either from limited data privacy, or from their excessive processing demands for constrained distributed devices. In this paper, we present Camouflage learning, a distributed machine learning scheme that obscures the trained model via probabilistic collaboration using physical-layer computation offloading and demonstrate the feasibility of the approach on backscatter communication prototypes and in comparison with Federated learning. We show that Camouflage learning is more energy efficient than traditional schemes and that it requires less communication overhead while reducing the computation load through physical-layer computation offloading. The scheme is synchronization-agnostic and thus appropriate for sharply constrained, synchronization-incapable devices. We demonstrate model training and inference on four distinct datasets and investigate the performance of the scheme with respect to communication range, impact of challenging communication environments, power consumption, and the backscatter hardware prototype.
Description
Keywords
Other note
Citation
Nguyen , L N , Sigg , S , Lietzén , J , Findling , R D & Ruttik , K 2021 , ' Camouflage learning : Feature value obscuringambient intelligence for constrained devices ' , IEEE Transactions on Mobile Computing , vol. 22 , no. 2 , pp. 781-796 . https://doi.org/10.1109/TMC.2021.3092271