Publication

International Conference on Machine Learning & Applications (CMLA 2020)

David Wyld et al. (editor)

International Conference on Machine Learning and Applications (ICMLA-2020) 2nd International Conference on Machine Learning & Applications (CMLA 2020) September 26-27 Copenhagen, Denmark Denmark DFKI Research Reports (RR) Computer Science Conference Proceedings in Computer Science & Information Technology (CS & IT) 9/2020.

Abstract

One of the significant challenges of Artificial Intelligence (AI) and Machine learning models is to preserve data privacy and to ensure data security. Addressing this problem lead to the application of Federated Learning (FL) mechanism towards preserving data privacy. Preserving user privacy in the European Union (EU) has to abide by the General Data Protection Regulation (GDPR). Therefore, exploring the machine learning models for preserving data privacy has to take into consideration of GDPR. In this paper, we present in detail understanding of Federated Machine Learning, various federated architectures along with different privacy-preserving mechanisms. The main goal of this survey work is to highlight the existing privacy techniques and also propose applications of Federated Learning in Industries. Finally, we also depict how Federated Learning is an emerging area of future research that would bring a new era in AI and Machine learning.

Projekte

Weitere Links

CMLA_Camera_Ready_Sheela_Kurupathi.pdf (pdf, 522 KB)

German Research Center for Artificial Intelligence
Deutsches Forschungszentrum für Künstliche Intelligenz