Coordinating Direction Filtering, Reputation Aggregation, and Adaptive Differential Privacy: A Backdoor Defense Framework for Federated Object Detection in Remote Sensing

Federated object detection over multi-site remote-sensing imagery faces two coupled risks that are not fully addressed by existing methods. Existing federated object-detection studies mainly focus on distributed training, communication efficiency, and non-IID heterogeneity, while many robust aggregation rules rely on magnitude or coordinate statistics and can miss directional object-disappearance attacks in which the malicious update suppresses target boxes without an obvious test-time patch. Static differential privacy further applies uniform perturbation across clients, which may reduce detection utility and does not use trust differences among participants. We propose DUAL-SHIELD, a defense-and-privacy pipeline linked by three round-wise interfaces: a cosine gradient filter that detects directional anomalies with a median-absolute-deviation threshold; a momentum-based reputation aggregator that converts per-round filtering decisions into a continuous trust state; and a reputation-aware adaptive differential-privacy scheduler that allocates client-specific perturbation under explicit cumulative accounting. In the default NWPU VHR-10 object-disappearance setting, the framework keeps clean detection performance at 0.64 and reduces the attack success rate from 0.95 to 0.04. Additional transfer and attack-family evaluations are reported as supporting checks rather than definitive generalization claims, and the corresponding claims are limited to the configurations with matched-seed experiments, completed privacy accounting, and available baseline comparisons.