Camouflage learning: Feature value obscuringambient intelligence for constrained devices

Le Ngu Nguyen, Stephan Sigg, Jari Lietzén, Rainhard Dieter Findling, Kalle Ruttik

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)
69 Downloads (Pure)


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.
Original languageEnglish
Pages (from-to)781-796
Number of pages17
JournalIEEE Transactions on Mobile Computing
Issue number2
Publication statusE-pub ahead of print - 24 Jun 2021
MoE publication typeA1 Journal article-refereed


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