An Efficient Industrial Federated Learning Framework for AIoT: A Face Recognition Application

TL;DR

This paper introduces an efficient federated learning framework tailored for AIoT face recognition, leveraging transfer learning and a private projector to enhance privacy, speed, and accuracy in a privacy-preserving, resource-constrained environment.

Contribution

It presents a novel federated learning framework with transfer learning and a private projector, improving training speed, privacy, and accuracy for AIoT face recognition applications.

Findings

Achieves high recognition accuracy in 20 communication rounds.

Demonstrates effectiveness in prediction accuracy.

Shows efficiency in training with privacy protection.

Abstract

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.