GA-AFedOD: gradient-aligned active federated learning for resource-aware object detection in edge industrial IoT
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 pr
Record details
Published: 10 August 2026
Source: Frontiers in Artificial Intelligence
Category: Research
Topics: Safety & alignment · Privacy
Retrieved: 11 August 2026
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ethics.ai (10 August 2026), “GA-AFedOD: gradient-aligned active federated learning for resource-aware object detection in edge industrial IoT,” evidence record 18076, https://ethics.ai/record/18076 (originally published by Frontiers in Artificial Intelligence).
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