面向智能交通的实时目标检测，实现去中心化隐私保护与高效通信。
BlocksecRT-DETR: Decentralized Privacy-Preserving and Token-Efficient Federated Transformer Learning for Secure Real-Time Object Detection in ITS
- 在客户端融合轻量化令牌剪枝与实时检测模型，降低边缘设备延迟。
- 令牌工程模块使推理延迟降17.2%，编码器计算量减少47.8%，保持89.20% [email protected]精度。
- 基于区块链的去中心化验证机制，每轮增加400毫秒开销，链上存储仅12KB。
在智能交通系统中，基于Transformer的联邦实时目标检测面临三大挑战：（1）地理分布导致的缺失类别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；（3）不受信客户端更新与集中式聚合带来的隐私与安全风险。本文提出BlockSecRT-DETR框架，采用基于区块链的安全验证机制，实现去中心化、隐私保护且高效的联邦训练。该框架通过统一客户端设计，将RT-DETR训练与令牌工程模块（TEM）结合：TEM剪枝低效令牌，降低编码器复杂度与延迟，同时缓解跨客户端数据异质性。针对隐私安全问题，引入去中心化的区块链验证机制，实现无需信任中心服务器的防篡改、隐私保护模型聚合。在KITTI数据集的缺失类别非独立同分布划分下评估，TEM使推理延迟降低17.2%，编码器浮点运算量减少47.8%，全局检测精度维持在89.20% [email protected]。区块链集成每轮增加400毫秒开销，由于仅存储元数据，链上数据量保持在12 KB以内。
原文摘要 · Abstract (English)
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% [email protected]). The blockchain integration adds 400 ms per round, and the ledger size remains under 12 KB due to metadata-only on-chain storage.
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