Publikation

Survey on Federated Learning towards Privacy Preserving AI

Sheela Raju Kurupathi, Wolfgang Maaß

In: David Wyld et al. (Hrsg.). International Conference on Machine Learning & Applications (CMLA 2020). International Conference on Machine Learning and Applications (ICMLA-2020) 2nd September 26-27 Copenhagen Denmark Seiten 235-253 10 11 Computer Science Conference Proceedings in Computer Science & Information Technology (CS & IT) 9/2020.

Abstrakt

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

csit101120.pdf (pdf, 793 KB)

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence