A distillation-based approach integrating continual learning and federated learning for pervasive services

Abstract

Federated Learning, a new machine learning paradigm enhancing the use of edge devices, is receiving a lot of attention in the pervasive community to support the development of smart services. Nevertheless, this approach still needs to be adapted to the specificity of the pervasive domain. In particular, issues related to continual learning need to be addressed. In this paper, we present a distillation-based approach dealing with catastrophic forgetting in federated learning scenario. Specifically, Human Activity Recognition tasks are used as a demonstration domain.

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Cited by in corpus (6)

• Federated Continual Learning via Knowledge Fusion: A Survey
• Federated Continual Learning: Concepts, Challenges, and Solutions
• Evaluation and comparison of federated learning algorithms for Human Activity Recognition on smartphones
• Speeding up Heterogeneous Federated Learning with Sequentially Trained Superclients
• Continual Horizontal Federated Learning for Heterogeneous Data
• Federated Learning and catastrophic forgetting in pervasive computing: demonstration in HAR domain