Scholarlink

We present a federated edge-intelligence framework for smart-mobility cybersecurity that integrates Edge AI, Federated Learning (FL), and blockchain anchoring, and we provide a runnable artifact for full reproducibility. Using a synthetic IDS-like workload with non-IID client splits, we benchmark centralised, edge-only, and FL (FedAvg) training while accounting for communication, a latency proxy, and a FLOPs-based energy index. FL maintained near-centralised accuracy (≈99.8%) and F1 (0.9932–0.9938), whereas edge-only degraded under client skew (≈85.3% accuracy; F1 ≈ 0). Training-time communication for FL was 98.96% lower than centralised at 5 clients/10 rounds (0.033 MB vs. 3.206 MB) and 97.99% lower at 10 clients/10 rounds (0.065 MB vs. 3.206 MB). The latency proxy grows linearly with FL rounds yet remains well below centralised inference (132 ms vs. 3,301 ms at 5 clients/10 rounds). Energy results follow expectations: edge-only lowest (0.000832), centralised mid (0.001040), and FL highest due to local training (0.001300–0.001560). Overall, the results quantify accuracy/communication/latency/energy trade-offs and show that federated-edge learning preserves accuracy under client heterogeneity while minimizing raw-data transfer; blockchain anchoring adds only a small, parameterized per-commit overhead. All configurations and logs are released to enable exact reproduction.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Federated Edge Intelligence with Blockchain Anchoring for Privacy-Preserving Smart Mobility Over 5G

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Manuel J. C. S. Reis,
  • Carlos Serôdio,
  • Luís Correia,
  • Frederico Branco

摘要

We present a federated edge-intelligence framework for smart-mobility cybersecurity that integrates Edge AI, Federated Learning (FL), and blockchain anchoring, and we provide a runnable artifact for full reproducibility. Using a synthetic IDS-like workload with non-IID client splits, we benchmark centralised, edge-only, and FL (FedAvg) training while accounting for communication, a latency proxy, and a FLOPs-based energy index. FL maintained near-centralised accuracy (≈99.8%) and F1 (0.9932–0.9938), whereas edge-only degraded under client skew (≈85.3% accuracy; F1 ≈ 0). Training-time communication for FL was 98.96% lower than centralised at 5 clients/10 rounds (0.033 MB vs. 3.206 MB) and 97.99% lower at 10 clients/10 rounds (0.065 MB vs. 3.206 MB). The latency proxy grows linearly with FL rounds yet remains well below centralised inference (132 ms vs. 3,301 ms at 5 clients/10 rounds). Energy results follow expectations: edge-only lowest (0.000832), centralised mid (0.001040), and FL highest due to local training (0.001300–0.001560). Overall, the results quantify accuracy/communication/latency/energy trade-offs and show that federated-edge learning preserves accuracy under client heterogeneity while minimizing raw-data transfer; blockchain anchoring adds only a small, parameterized per-commit overhead. All configurations and logs are released to enable exact reproduction.