A chaotic maps-based privacy-preserving distributed deep learning for incomplete and Non-IID datasets

arXiv:2402.10145v1 Announce Type: cross
Abstract: Federated Learning is a machine learning approach that enables the training of a deep learning model among several participants with sensitive data that wish to share their own knowledge without compromising the privacy of their data. In this research, the authors employ a secured Federated Learning method with an additional layer of privacy and proposes a method for addressing the non-IID challenge. Moreover, differential privacy is compared with chaotic-based encryption as layer of privacy. The …

arxiv authors cs.cr cs.dc cs.lg data datasets deep learning distributed federated federated learning knowledge machine machine learning maps non own privacy research sensitive sensitive data share training