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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 …