Abstract

Federated learning (FL) enables privacy-preserving fault diagnosis across distributed industrial devices, but most existing methods assume homogeneous model architectures and comparable client resources. This assumption is unrealistic in resource-constrained Industrial Internet of Things (IIoT) scenarios, where clients may have substantially different memory and computation capacities. To address this challenge, we propose FedAnchor, an anchored and adaptive FL framework for resource-heterogeneous fault diagnosis. FedAnchor decomposes each client submodel into a shared anchored core and a client-specific adaptive extension. The anchored core provides a common parameter subspace for consistent masked aggregation, while the adaptive extension is selected by a server-side reinforcement-guided policy under client memory budgets. This design couples resource-aware submodel allocation with structurally aligned aggregation. Experiments on four benchmark datasets and six heterogeneous memory configurations show that FedAnchor achieves competitive or superior accuracy compared with representative homogeneous and model-heterogeneous FL baselines. Under the evaluated non-IID settings, FedAnchor improves accuracy by up to 9.8 percentage points over the strongest baseline, while maintaining favorable communication–accuracy trade-offs and empirical stability.

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