SecureDrive‑FL combines differential privacy (DP‑SGD) with a novel Gradient‑Aware Selective Homomorphic Encryption (GASHE) scheme to protect federated driver‑monitoring models. GASHE encrypts only gradient components that exceed a DP‑calibrated sensitivity threshold, avoiding full‑parameter encryption. In experiments on a ten‑class distracted driver task, SecureDrive‑FL matches DP‑SGD’s poisoning resistance while also defending against Man‑in‑the‑Middle attacks, adding only 8–10% runtime overhead.
The paper investigates how federated learning (FL) updates in vehicular edge networks can reveal client identities through gradient-based attacks on inertial sensor data, using the UCI Human Activity Recognition benchmark as a proxy. Experiments show that an honest-but-curious server can identify clients with near-perfect accuracy from unprotected updates. The authors evaluate lightweight defenses—clipping followed by Gaussian noise and ensemble FL—to mitigate this privacy risk while preserving model utility, reporting differential‑privacy budgets and empirical results across multiple attack classifiers and data partitions.
By Ali Akarma (Islamic University of Madinah, Madinah, Saudi Arabia, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia), Toqeer Ali Syed (Islamic University of Madinah, Madinah, Saudi Arabia), Muhammad Khan (University of the West of England, Bristol, U.K), Qurat-ul-ain Mastoi (University of the West of England, Bristol, U.K), Adeel Ahmad (Islamic University of Madinah, Madinah, Saudi Arabia)
arXiv:2412. 12640v2 Announce Type: replace Abstract: The increasing demand for data privacy, alongside the benefits of aggregating data from networked devices, has catalyzed the emergence of federated learning (FL).
By Rui Zhang, Ka-Ho Chow
arXiv:2509. 10691v3 Announce Type: replace-cross Abstract: Decentralized federated learning enables collaborative model training without a central server, but shared model updates can still leak sensitive information through inversion, reconstruction, and membership inference attacks.
By Fardin Jalil Piran, Zhiling Chen, Yang Zhang, Qianyu Zhou, Jiong Tang, Farhad Imani
arXiv:2608. 15153v1 Announce Type: cross Abstract: Differentially private federated learning must balance privacy protection against model accuracy and training efficiency.
By Wenjing Wei, Alla Jammine, Farid Nait-Abdesselam
arXiv:2606. 02563v1 Announce Type: new Abstract: Heterogeneous Differential Privacy (HDP) in Federated Learning (FL) allows clients to select individual privacy budgets ($\varepsilon_i$) according to institutional policies and data sensitivity.
By Farhin Farhad Riya, Olivera Kotevska, Jinyuan Stella Sun