J. Leskovec, A. Singh, and J. Kleinberg, Patterns of Influence in a Recommendation Network, Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp.380-389, 2006.
DOI : 10.1007/11731139_44

J. Leskovec, M. Mcglohon, C. Faloutsos, N. Glance, and M. Hurst, Patterns of Cascading Behavior in Large Blog Graphs, Proceedings of the 2007 SIAM international conference on data mining, pp.551-556, 2007.
DOI : 10.1137/1.9781611972771.60

J. Leskovec and C. Faloutsos, Sampling from large graphs, Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '06, pp.631-636, 2006.
DOI : 10.1145/1150402.1150479

M. Al-fares, A. Loukissas, and A. Vahdat, A scalable, commodity data center network architecture, ACM SIGCOMM Computer Communication Review, vol.38, issue.4, pp.63-74, 2008.
DOI : 10.1145/1402946.1402967
URL : http://www.cs.ucsd.edu/~vahdat/papers/sigcomm08.pdf

A. Holzinger, Introduction to MAchine Learning & Knowledge Extraction (MAKE), Machine Learning and Knowledge Extraction, vol.15, issue.1, pp.1-20, 2017.
DOI : 10.1007/978-3-642-40511-2_22

H. Brendan-mcmahan, E. Moore, D. Ramage, and S. Hampson, Communication-efficient learning of deep networks from decentralized data, 2016.

J. Kone?n-`-kone?n-`-y,-h-brendan-mcmahan, X. Felix, P. Yu, A. T. Richtárik, D. Suresh et al., Federated learning: Strategies for improving communication efficiency. arXiv preprint, 2016.

J. Kone?n-`-kone?n-`-y,-h-brendan-mcmahan, D. Ramage, and P. Richtárik, Federated optimization: distributed machine learning for on-device intelligence, 2016.

K. Bonawitz, V. Ivanov, B. Kreuter, A. Marcedone, H. B. Mcmahan et al., Practical Secure Aggregation for Privacy-Preserving Machine Learning, Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security , CCS '17, p.281, 2017.
DOI : 10.1145/359168.359176

J. Martin, . Wainwright, I. Michael, . Jordan, C. John et al., Privacy aware learning, Advances in Neural Information Processing Systems, pp.1430-1438, 2012.

B. Malle, P. Kieseberg, E. Weippl, and A. Holzinger, The Right to Be Forgotten: Towards Machine Learning on Perturbed Knowledge Bases, International Conference on Availability, Reliability, and Security, pp.251-266, 2016.
DOI : 10.1145/1540276.1540279
URL : https://hal.archives-ouvertes.fr/hal-01635002

A. Holzinger, Interactive machine learning for health informatics: when do we need the human-in-the-loop?, Brain Informatics, vol.7, issue.1, pp.119-131, 2016.
DOI : 10.1007/978-3-540-69868-5_1

P. Kieseberg, B. Malle, P. Frhwirt, E. Weippl, and A. Holzinger, A tamper-proof audit and control system for the doctor in the loop, Brain Informatics, vol.53, issue.4, pp.1-11, 2016.
DOI : 10.1109/ARES.2012.50

A. Holzinger, . Plass, . Holzinger, . Gc-crisan, V. Pintea et al., Towards interactive Machine Learning (iML): Applying Ant Colony Algorithms to Solve the Traveling Salesman Problem with the Human-in-the-Loop Approach, IFIP International Cross Domain Conference and Workshop (CD-ARES), pp.81-95
DOI : 10.1016/j.compedu.2005.11.026
URL : https://hal.archives-ouvertes.fr/hal-01635020

L. Breiman, Bagging predictors, Machine Learning, vol.10, issue.2, pp.123-140, 1996.
DOI : 10.2307/1403680

N. Zhu, Data visualization with D3. js cookbook, 2013.

H. Alani, S. Kim, E. David, . Millard, J. Mark et al., Automatic ontology-based knowledge extraction from Web documents, IEEE Intelligent Systems, vol.18, issue.1, pp.14-21, 2003.
DOI : 10.1109/MIS.2003.1179189
URL : http://www.equator.ac.uk/PublicationStore/2002-alani.pdf