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
The paper introduces GASHE, a gradient‑aware selective homomorphic encryption scheme that encrypts only those gradient components exceeding a differential‑privacy‑calibrated sensitivity threshold, rather than encrypting all parameters. Building on GASHE, SecureDrive‑FL combines DP‑SGD with this selective encryption to form a closed‑loop DP+HE privacy pipeline for federated driver monitoring. Experiments on a ten‑class distracted driver classification task show that SecureDrive‑FL retains the poisoning resistance of DP‑SGD while also defending against Man‑in‑the‑Middle interception, with only an 8–10% runtime overhead.
By Baran Can G\"ul, Hanuma Siddhartha Tunuguntla, Anjana Arvind Naik, Abhishek Vijay Potekar, Nasser Jazdi, Michael Weyrich
Heterogeneous Differential Privacy (HDP) in Federated Learning (FL) allows clients to select individual privacy budgets ($\varepsilon_i$) according to institutional policies and data sensitivity. In practice, many HDP-FL systems employ $\varepsilon$-aware server aggregation to improve model utility by re-weighting client updates according to their declared privacy budgets.
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 proposes a communication‑efficient private federated reinforcement learning scheme for vehicular networks that uses correlated private quantization and post‑quantum authentication. By sharing complementary randomness between paired vehicles, the method reduces aggregation mean‑squared error while maintaining one‑bit communication and local differential privacy. Experiments show significant uplink savings, improved routing performance, and reduced privacy leakage compared to independent quantization.
By Hojat Allah Salehi, Devrim Aras, Md Jueal Mia, M. Hadi Amini, Imtiaz Karim, Mashrur Chowdhury, Bhavani Thuraisingham
The paper investigates how privacy guarantees, robustness to Byzantine attacks, and detection coverage for rare intrusion types interact in federated network intrusion detection systems. It introduces geometric indistinguishability to explain how privacy noise can obscure minority-class signals, and demonstrates through experiments on UNSW‑NB15 that combining differential privacy with robust aggregation can disproportionately harm detection of rare attacks. The study highlights that these properties cannot be treated as independently composable and calls for aggregation‑aware modeling and sample‑aware evaluation to build trustworthy federated NIDS.
By Adrita Rahman Tory, ABM Shawkat Ali, Md Abu Layek, Khondokar Fida Hasan