Personalized Federated Continual Learning for Task-Incremental Biometrics

In the age of Internet of Things where information is explosively growing, people pay more attention on personal privacy. In the real-world task-incremental scenario for biometrics, every edge device faces continuous task flows of private data without communication with others. security and performance are the primary concerns in identity authentication, and federated continual learning (FCL) is a promising solution. In this article, we design a personalized FCL framework to solve the problem of sequential identification in every distributed device. For each client, we create an adaptive continual metalearning model called continual task-distillation-based adaptive model-agnostic metalearning (cTD-
$\alpha $
MAML), aiming to align the gradients of previous and new tasks and to make the learning-rate (LR) model learnable. For central aggregation, the server gathers the metainitialization from every local update and allocates the updated global metainitialization to clients. We propose an extension of federated average to locally reserve the learnable LR network to realize the personalization of clients. Results prove that in continual learning, our cTD-
$\alpha $
MAML can learn to learn the seen tasks and avoid catastrophic forgetting. And in FCL, our personalized method realizes the knowledge transferring across clients, meanwhile improving the local performance and reducing the communication cost. In this way, the proposed personalized FCL framework can obtain a biometric template that is able to learn the expression space for new tasks with rapid adaption.