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Energy-Efficient Pr...
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Arsalan, MuhammadTech Univ Carolo Wilhelmina Braunschweig, Braunschweig, Germany.
(författare)
Energy-Efficient Privacy-Preserving Time-Series Forecasting on User Health Data Streams
- Artikel/kapitelEngelska2022
Förlag, utgivningsår, omfång ...
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Institute of Electrical and Electronics Engineers (IEEE),2022
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Nummerbeteckningar
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LIBRIS-ID:oai:DiVA.org:kth-331213
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https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-331213URI
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https://doi.org/10.1109/TrustCom56396.2022.00080DOI
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Språk:engelska
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Sammanfattning på:engelska
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QC 20230706
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Health monitoring devices are gaining popularity both as wellness tools and as a source of information for healthcare decisions. In this work, we use Spiking Neural Networks (SNNs) for time-series forecasting due to their proven energy-saving capabilities. Thanks to their design that closely mimics the natural nervous system, SNNs are energy-efficient in contrast to classic Artificial Neural Networks (ANNs). We design and implement an energy-efficient privacy-preserving forecasting system on real-world health data streams using SNNs and compare it to a state-of-the-art system with Long short-term memory (LSTM) based prediction model. Our evaluation shows that SNNs tradeoff accuracy (2.2x greater error), to grant a smaller model (19% fewer parameters and 77% less memory consumption) and a 43% less training time. Our model is estimated to consume 3.36 mu J energy, which is significantly less than the traditional ANNs. Finally, we apply epsilon-differential privacy for enhanced privacy guarantees on our federated learning-based models. With differential privacy of epsilon = 0.1, our experiments report an increase in the measured average error (RMSE) of only 25%.
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Biuppslag (personer, institutioner, konferenser, titlar ...)
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Di Matteo, DavideKTH(Swepub:kth)u168b8r6
(författare)
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Imtiaz, SanaKTH,Programvaruteknik och datorsystem, SCS,KRY Int AB, Stockholm, Sweden.(Swepub:kth)u1fqkcg4
(författare)
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Abbas, ZainabKTH,Programvaruteknik och datorsystem, SCS,KRY Int AB, Stockholm, Sweden.(Swepub:kth)u1pk16vj
(författare)
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Vlassov, Vladimir,1957-KTH,Programvaruteknik och datorsystem, SCS(Swepub:kth)u19yb2c8
(författare)
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Issakov, VadimTech Univ Carolo Wilhelmina Braunschweig, Braunschweig, Germany.
(författare)
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KTHTech Univ Carolo Wilhelmina Braunschweig, Braunschweig, Germany.
(creator_code:org_t)
Sammanhörande titlar
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Ingår i:Proceedings - 2022 IEEE 21st International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2022: Institute of Electrical and Electronics Engineers (IEEE), s. 541-546
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