  With the advances in 5G and IoT devices, the industries are vastly adopting
artificial intelligence (AI) techniques for improving classification and
prediction-based services. However, the use of AI also raises concerns
regarding privacy and security that can be misused or leaked. Private AI was
recently coined to address the data security issue by combining AI with
encryption techniques, but existing studies have shown that model inversion
attacks can be used to reverse engineer the images from model parameters. In
this regard, we propose a Federated Learning and Encryption-based Private
(FLEP) AI framework that provides two-tier security for data and model
parameters in an IIoT environment. We proposed a three-layer encryption method
for data security and provide a hypothetical method to secure the model
parameters. Experimental results show that the proposed method achieves better
encryption quality at the expense of slightly increased execution time. We also
highlight several open issues and challenges regarding the FLEP AI framework's
realization.
