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A distillation-based approach integrating continual learning and federated learning for pervasive services
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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 5 Pith papers
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Server-Proximal Aggregation for Federated Domain-Incremental Learning under Partial Participation: Task-Uniform Convergence and Backward Transfer
A server-side proximal anchor on FedAvg provably limits forgetting and gives a task-uniform convergence rate of O(sqrt(E/(NT))) for federated domain-incremental learning under partial participation.
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Knowledge-Aware Evolution for Task-Free Streaming Federated Continual Learning with Arbitrary Class Overlap
FedKACE combines adaptive inference-model switching, gradient-ratio replay weighting, and kernel-based buffer selection and reports the best average accuracy and regret in task-free streaming federated continual learn...
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TaskVAE: Task-Specific Variational Autoencoders for Exemplar Generation in Continual Learning for Human Activity Recognition
Task-specific VAEs that generate filtered synthetic samples outperform memory replay in class-incremental human activity recognition.
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Federated Continual Learning for Edge-AI: A Comprehensive Survey
A survey that organizes federated continual learning methods into class, domain, and task continual learning categories for edge-AI applications.
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Federated Continual Learning: Concepts, Challenges, and Solutions
A literature review that categorizes challenges and solutions in federated continual learning and adds an experimental comparison of aggregation strategies.
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