A Survey on Secure and Private Federated Learning Using Blockchain: Theory and Application in Resource-constrained Computing. (arXiv:2303.13727v1 [cs.CR])

Federated Learning (FL) has gained widespread popularity in recent years due
to the fast booming of advanced machine learning and artificial intelligence
along with emerging security and privacy threats. FL enables efficient model
generation from local data storage of the edge devices without revealing the
sensitive data to any entities. While this paradigm partly mitigates the
privacy issues of users' sensitive data, the performance of the FL process can
be threatened and reached a bottleneck due to the growing cyber …

advanced application artificial artificial intelligence blockchain computing cyber cyber threats data data storage devices edge edge devices emerging entities fast federated learning intelligence local machine machine learning paradigm performance privacy private process security sensitive data storage survey the edge theory threats