Fast Meta Failure Recovery for Federated Meta-Learning

Publication Name

Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023


In recent years, the field of distributed deep learning within the Internet of Things (IoT) or the edge has experienced exponential growth. Federated meta-learning has emerged as a significant advancement, enabling collaborative learning among source nodes to establish a global model initialization. This approach allows for optimal performance while necessitating minimal data samples for updating model parameters at the target node. Federated meta-learning has gained increased attention due to its capacity to provide real-time edge intelligence. However, a critical aspect that remains inadequately explored is the recovery of interim meta knowledge's failure, which constitutes a pivotal key for adapting to new tasks. In this paper, we introduce FMRec, a novel platform designed to offer a fast and flexible recovery mechanism for failed interim meta knowledge in various federated meta-learning scenarios. FMRec serves as a complementary system compatible with different types of federated models and is adaptable to diverse tasks. We present a demonstration of its design and assess its efficiency and reliability through real-world applications.

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