SkyCL: Swift Continuous Learning with Kinship-Awareness for Multi-Drone Video Analytics under Drastic Drift

Drones are increasingly used for on-device video analytics, yet their limited resources can only support lightweight DNNs. To enhance on-device models, continuous learning has been proposed that adapts DNNs to real-time videos by model reuse or retraining. However, existing methods struggle with low accuracy due to drastic drift in agile drones' videos, which leads to outdated retraining, false alarm in drift detection, and intricate performance kinship within the historical model zoo. In this paper, we propose SkyCL, a swift continuous learning system to enhance multi-drone video analytics under drastic drift. SkyCL achieves swift on-device adaptation by dual-level drift detection and distributed retraining scheduling across multiple edge devices. Moreover, SkyCL leverages model kinship within the model zoo to discover a hidden path of models' improvement for potential optimal reuse. We evaluate SkyCL in both simulated and real drone systems, with results showing a 14.39% accuracy gain and a 2.41× speedup over baselines.