Federated Learning for Object Detection: Enabling Collaborative Drone Learning Without Centralizing Data
This paper applies Federated Learning (FL) to object detection in drone networks, allowing drones to collaboratively train a shared model without sharing raw aerial imagery. Using the Sherpa.ai FL platform on the KIIT-MiTA dataset, the authors compare FL with single-drone and centralized baselines. Their best lightweight model (YOLO26 nano) achieves relative gains of 52.89% in [email protected] and 67.80% in [email protected]:0.95 over single-drone training, while remaining close to centralized performance. The results demonstrate that FL enables scalable, high-performing, and privacy-preserving object detection across distributed drone fleets.
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[Submitted on 2 Jul 2026]
Title:Federated Learning for Object Detection: Enabling Collaborative Drone Learning Without Centralizing Data
View a PDF of the paper titled Federated Learning for Object Detection: Enabling Collaborative Drone Learning Without Centralizing Data, by Daniel M. Jimenez-Gutierrez and 5 other authors
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Abstract:Object detection is a fundamental capability for AI-driven perception in safety-critical drone and edge-vision systems, including disaster response, operational security environments, infrastructure monitoring and defense applications. Robust model performance in such environments depends on large, continuously updated datasets. However, training high-performing detectors typically requires centralizing aerial imagery, which raises privacy, regulatory, storage, and bandwidth challenges. This is especially problematic in distributed drone deployments, where visual data is generated onboard and is often impractical or undesirable to transfer to a centralized infrastructure.
In this work, we apply Federated Learning (FL) for object detection, enabling drones to improve a shared model while keeping image data local and private. We implement a federated object detection pipeline using the this http URL FL platform on the KIIT-MiTA dataset, and compare it with Single-drone and Centralized baselines using mean Average Precision (mAP) at IoU thresholds of 0.50 and 0.50-0.95. In our experiments, the proposed FL approach remains close to Centralized training while dramatically improving over Single-drone training, with the best lightweight model (YOLO26 nano), suitable for deployment even on very limited edge infrastructure, achieving relative gains of 52.89% and 67.80% in [email protected] and [email protected]:0.95, respectively. These results show that FL enables scalable, high-performing, and privacy-preserving object detection across distributed drone fleets without data centralization.
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Distributed, Parallel, and Cluster Computing (cs.DC)
Cite as: arXiv:2607.02636 [cs.LG]
(or arXiv:2607.02636v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.02636
arXiv-issued DOI via DataCite
Submission history
From: Joaquin Del Rio [view email] [v1] Thu, 2 Jul 2026 15:32:37 UTC (4,237 KB)
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