Prompt transfer for few-shot fault diagnosis in IIoT: a federated learning framework with tabular foundation models
In industrial internet of things (IIoT) deployments, condition monitoring data is rarely complete. Labeled samples are scarce, sensors drop out, signals are corrupted by noise, and what reaches the edge is often a sparse, distorted fragment of the ideal dataset. Federated learning preserves privacy, yet its standard formulations assume clients can train locally on adequate labeled data-an assumption that seldom holds on the factory floor. This paper proposes prompt transfer, a distinct federated learning paradigm that replaces conventional gradient or prototype exchange with the transfer of small, curated sets of labeled examples (prompts) as the sole medium of knowledge flow. Unlike federated distillation, which shares model outputs, or meta-learning, which requires local gradient adaptation, prompt transfer leverages the in-context learning capability of a frozen tabular foundation model to achieve zero-gradient, parameter-free knowledge transfer across clients. Experiments on the Southeast University bearing dataset show that under 5-shot settings, our method FedICL achieves 94.0% accuracy, in contrast to FedAvg’s random-guessing performance of 20.1% while requiring zero gradient computation and reducing communication rounds by 95% and data transfer by 87%. The approach remains effective under non-IID distributions ( 53.9% vs 20% random). This work directly addresses the special issue themes of incomplete data and federated learning, offering a practical solution for label-scarce, resource-constrained IIoT scenarios.