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GA-AFedOD: gradient-aligned active federated learning for resource-aware object detection in edge industrial IoT
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DOI：10.3389/frai.2026.1895239.png)
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Abstract
En 中文
Visual object detection is essential for defect inspection and process monitoring in edge-deployed Industrial Internet of Things (IIoT). Yet; training accurate detectors across distributed factories faces stringent constraints on data privacy; annotation budgets; and uplink communication. Standard federated learning (FL) preserves locality but often wastes labeling resources on redundant frames and overlooks detection-specific gradient alignment when scheduling clients. To bridge this gap; we propose Gradient-Aligned Active Federated Object Detection (GA-AFedOD); a unified framework that jointly optimizes annotation selection; client participation; and model aggregation as a constrained stochastic program. A novel utility metric integrates box-level uncertainty; prototype diversity; gradient alignment; and resource pricing; enabling edge clients to perform locally guided active querying while the server solves a lightweight primal-dual problem for budget-aware client scheduling. We prove a submodular approximation guarantee for the greedy sampling rule and establish a non-convex convergence bound that explicitly captures the impact of label budgets; client drift; and compression noise. This article further clarifies the relationship with recent federated active learning and industrial detection studies; adds parameter and theory-diagnostic analyses; and distinguishes controlled simulation evidence from real-world deployment validation on industrial datasets such as RasPiDets; Electric Power Fitting Dataset (EPFD); and Diverse Insulator Dataset (DINS). Controlled simulation results show that GA-AFedOD achieves considerably higher mean average precision (mAP) while reducing both annotation costs and uplink consumption by over 40% compared with competitive baselines.
Keywords:
object detection
active learning
federated learning
industrial IoT
edge intelligence
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