nach oben

Erschienen in:

2019 | OriginalPaper | Buchkapitel

Train Global, Test Local: Privacy-Preserving Learning of Cost-Effectiveness in Decentralized Systems

verfasst von : Jovan Nikolić, Marcel Schöengens, Evangelos Pournaras

Erschienen in: Advances in Intelligent Networking and Collaborative Systems

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

The mandate of citizens for more socially responsible information systems that respect privacy and autonomy calls for a computational and storage decentralization. Crowd-sourced sensor networks monitor energy consumption and traffic jams. Distributed ledgers systems provide unprecedented opportunities to perform secure peer-to-peer transactions using blockchain. However, decentralized systems often show performance bottlenecks that undermine their broader adoption: propagating information in a network is costly and time-consuming. Optimization of cost-effectiveness with supervised machine learning is challenging. Training usually requires privacy-sensitive local data, for instance, adjusting the communication rate based on citizens’ mobility. This paper studies the following research question: How feasible is to train with privacy-preserving aggregate data and test on local data to improve cost-effectiveness of a decentralized system? Centralized machine learning optimization strategies are applied to DIAS, the Dynamic Intelligent Aggregation Service and they are compared to decentralized self-adaptive strategies that use local data instead. Experimental evaluation with a testing set of 2184 decentralized networks of 3000 nodes aggregating real-world Smart Grid data confirms the feasibility of a linear regression strategy to improve both estimation accuracy and communication cost, while the other optimization strategies show trade-offs.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Expert Knowledge-Based Authentication Protocols for Cloud Computing Applications

Nächstes Kapitel Performance Evaluation of WMNs for Normal and Uniform Distribution of Mesh Clients Using WMN-PSOSA Simulation System

Available af http://dias-net.org (last access: May 2018).

\(B_{U}=c\) assumes that gossiping updates the view at least once per epoch.

https://github.com/epournaras/DIAS and https://github.com/epournaras/PeerSamplingService (last access: May 2018).

https://www.cscs.ch (last access: May 2018).

https://scicomp.ethz.ch/wiki/Euler (last access: May 2018).

http://www.ucd.ie/issda/data/commissionforenergyregulationcer/ (last accessed: May 2018).

https://www.cs.waikato.ac.nz/ml/weka/ (last access: May 2018).

http://scikit-learn.org/stable/ (last access: May 2018).

Linear regression has an average precision, recall, f1-score of 0.8 and 0.96 for neural network. 273662 occurrences appear for save and 123557 for consume in linear regression. The respective occurences are 274108 and 123111 for neural network. Validation metrics documentation: http://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_recall_fscore_support.html (last access: May 2018).

Azimi, R., Sajedi, H.: A decentralized gossip based approach for data clustering in peer-to-peer networks. J. Parallel Distrib. Comput. 119, 64–80 (2018)CrossRef

Barjini, H., Othman, M., Ibrahim, H., Udzir, N.I.: Shortcoming, problems and analytical comparison for flooding-based search techniques in unstructured P2P networks. Peer-to-Peer Netw. Appl. 5(1), 1–13 (2012)CrossRef

Bhattarai, B., de Cerio Mendaza, I.D., Myers, K.S., Bak-Jensen, B., Paudyal, S.: Optimum aggregation and control of spatially distributed flexible resources in smart grid. IEEE Trans. Smart Grid (2017). https://ieeexplore.ieee.org/document/7886280/

Broder, A., Mitzenmacher, M.: Network applications of bloom filters: a survey. Int. Math. 1(4), 485–509 (2004)MathSciNetMATH

Dagar, M., Mahajan, S.: Data aggregation in wireless sensor network: a survey. Int. J. Inf. Comput. Technol. 3(3), 167–174 (2013)

Dietzel, S., Petit, J., Kargl, F., Scheuermann, B.: In-network aggregation for vehicular ad hoc networks. IEEE Commun. Surv. Tutorials 16(4), 1909–1932 (2014)CrossRef

Dwork, C., Roth, A., et al.: The algorithmic foundations of differential privacy. Found. Trends® Theor. Comput. Sci. 9(3–4), 211–407 (2014)MathSciNetMATH

Galuba, W., Aberer, K., Despotovic, Z., Kellerer, W.: ProtoPeer: a P2P toolkit bridging the gap between simulation and live deployement. In: Proceedings of the 2nd International Conference on Simulation Tools and Techniques, p. 60. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering) (2009)

Glorot, X., Bordes, A., Bengio, Y.: Deep sparse rectifier neural networks. In: Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, pp. 315–323 (2011)

10.

Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016)MathSciNetCrossRef

11.

Jelasity, M., Voulgaris, S., Guerraoui, R., Kermarrec, A.M., Van Steen, M.: Gossip-based peer sampling. ACM Trans. Comput. Syst. (TOCS) 25(3), 8 (2007)CrossRef

12.

Lee, A., Silvapulle, M.: Ridge estimation in logistic regression. Commun. Stat.-Simul. Comput. 17(4), 1231–1257 (1988)MathSciNetCrossRef

13.

Luo, L., Liu, M., Nelson, J., Ceze, L., Phanishayee, A., Krishnamurthy, A.: Motivating in-network aggregation for distributed deep neural network training. In: Workshop on Approximate Computing Across the Stack (2017)

14.

Parmar, P.V., Padhar, S.B., Patel, S.N., Bhatt, N.I., Jhaveri, R.H.: Survey of various homomorphic encryption algorithms and schemes. Int. J. Comput. Appl. 91(8), 26–32 (2014)

15.

Pilgerstorfer, P., Pournaras, E.: Self-adaptive learning in decentralized combinatorial optimization: a design paradigm for sharing economies. In: Proceedings of the 12th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, pp. 54–64. IEEE Press (2017)

16.

Pournaras, E., Nikolić, J.: On-demand self-adaptive data analytics in large-scale decentralized networks. In: 2017 IEEE 16th International Symposium on Network Computing and Applications (NCA), pp. 1–10. IEEE (2017)

17.

Pournaras, E., Nikolic, J.: Self-corrective dynamic networks via decentralized reverse computations. In: Proceedings of the 14th International Conference on Autonomic Computing (ICAC 2017) (2017)

18.

Pournaras, E., Nikolic, J., Omerzel, A., Helbing, D.: Engineering democratization in internet of things data analytics. In: Proceedings of the 31st IEEE International Conference on Advanced Information Networking and Applications-AINA-2017. IEEE (2017)

19.

Pournaras, E., Vasirani, M., Kooij, R.E., Aberer, K.: Measuring and controlling unfairness in decentralized planning of energy demand. In: 2014 IEEE International Energy Conference (ENERGYCON), pp. 1255–1262. IEEE (2014)

20.

Ramassamy, C., Fouchal, H.: A decision-support tool for wireless sensor networks. In: 2014 IEEE International Conference on Communications (ICC), pp. 7–11. IEEE (2014)

21.

Wu, J., Jia, Q.S., Johansson, K.H., Shi, L.: Event-based sensor data scheduling: trade-off between communication rate and estimation quality. IEEE Trans. Autom. Control 58(4), 1041–1046 (2013)MathSciNetCrossRef

22.

Xiao, Z., Song, W., Chen, Q.: Dynamic resource allocation using virtual machines for cloud computing environment. IEEE Trans. Parallel Distrib. Syst. 24(6), 1107–1117 (2013)CrossRef

Titel: Train Global, Test Local: Privacy-Preserving Learning of Cost-Effectiveness in Decentralized Systems
verfasst von: Jovan Nikolić
Marcel Schöengens
Evangelos Pournaras
Verlag: Springer International Publishing
Buch: Advances in Intelligent Networking and Collaborative Systems
Print ISBN: 978-3-319-98556-5

Electronic ISBN: 978-3-319-98557-2

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-319-98557-2_9

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner