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The scarcity of fault samples has been the bottleneck for the large-scale application of mechanical fault diagnosis (FD) methods in the industrial Internet of Things (IIoT). Traditional few-shot FD methods are fundamentally limited in that the models can only learn from the direct dataset, i.e., a limited number of local data samples. Federated learning (FL) has recently shown the capacity of collaborative artificial intelligence and privacy preservation. Based on these capabilities, we propose a novel approach to solve the few-shot FD problem, which includes a generic framework (i.e., FedMeta-FFD) and an easy-to-implement enhancement technique (i.e., AILR). The FedMeta-FFD framework allows clients to learn from indirect datasets owned by other collaborators while training a global meta-learner to solve the few-shot problem directly. More concretely, with only a few labeled examples and training iterations, the …