Federated real-time object detection using transformers in Intelligent Transportation Systems (ITS) faces three major challenges: (1) missing-class non-IID data heterogeneity from geographically diverse traffic environments, (2) latency constraints on edge hardware for high-capacity transformer models, and (3) privacy and security risks from untrusted client updates and centralized aggregation. We propose BlockSecRT-DETR, a BLOCKchain-SECured Real-Time Object DEtection TRansformer framework for ITS that provides a decentralized, token-efficient, and privacy-preserving federated training solution using RT-DETR transformer, incorporating a blockchain-secured update validation mechanism for trustworthy aggregation. In this framework, challenges (1) and (2) are jointly addressed through a unified client-side design that integrates RT-DETR training with a Token Engineering Module (TEM). TEM prunes low-utility tokens, reducing encoder complexity and latency on edge hardware, while aggregated updates mitigate non-IID data heterogeneity across clients. To address challenge (3), BlockSecRT-DETR incorporates a decentralized blockchain-secured update validation mechanism that enables tamper-proof, privacy-preserving, and trust-free authenticated model aggregation without relying on a central server. We evaluated the proposed framework under a missing-class Non-IID partition of the KITTI dataset and conducted a blockchain case study to quantify security overhead. TEM improves inference latency by 17.2% and reduces encoder FLOPs by 47.8%, while maintaining global detection accuracy (89.20% mAP@0.5). The blockchain integration adds 400 ms per round, and the ledger size remains under 12 KB due to metadata-only on-chain storage.

翻译：在智能交通系统中使用Transformer进行联邦实时目标检测面临三大挑战：(1) 来自地理分布广泛交通环境的缺失类非独立同分布数据异质性，(2) 高容量Transformer模型在边缘硬件上的延迟约束，以及(3) 不可信客户端更新与中心化聚合带来的隐私与安全风险。我们提出了BlockSecRT-DETR，一种面向ITS的区块链安全实时目标检测Transformer框架。该框架利用RT-DETR Transformer，提供了一种去中心化、令牌高效且隐私保护的联邦训练解决方案，并集成了区块链安全更新验证机制以实现可信聚合。在此框架中，挑战(1)和(2)通过一个统一的客户端设计共同解决，该设计将RT-DETR训练与令牌工程模块集成。TEM修剪低效用令牌，降低了边缘硬件上编码器的复杂性和延迟，同时聚合更新缓解了客户端间的非独立同分布数据异质性。为解决挑战(3)，BlockSecRT-DETR集成了一个去中心化的区块链安全更新验证机制，该机制无需依赖中心服务器，即可实现防篡改、隐私保护且无需信任的认证模型聚合。我们在KITTI数据集的缺失类非独立同分布划分下评估了所提框架，并进行了区块链案例研究以量化安全开销。TEM将推理延迟提升了17.2%，并将编码器FLOPs减少了47.8%，同时保持了全局检测精度（89.20% mAP@0.5）。区块链集成每轮增加400毫秒开销，并且由于仅将元数据存储在链上，账本大小保持在12 KB以下。