  Recently, the artificial intelligence of things (AIoT) has been gaining
increasing attention, with an intriguing vision of providing highly intelligent
services through the network connection of things, leading to an advanced
AI-driven ecology. However, recent regulatory restrictions on data privacy
preclude uploading sensitive local data to data centers and utilizing them in a
centralized approach. Directly applying federated learning algorithms in this
scenario could hardly meet the industrial requirements of both efficiency and
accuracy. Therefore, we propose an efficient industrial federated learning
framework for AIoT in terms of a face recognition application. Specifically, we
propose to utilize the concept of transfer learning to speed up federated
training on devices and further present a novel design of a private projector
that helps protect shared gradients without incurring additional memory
consumption or computational cost. Empirical studies on a private Asian face
dataset show that our approach can achieve high recognition accuracy in only 20
communication rounds, demonstrating its effectiveness in prediction and its
efficiency in training.
