The paper introduces SAFA-MZ, a secure adaptive federated authentication scheme for multi‑zone non‑terrestrial networks that embeds group‑level authentication tags into a collaborative rate‑splitting multiple access transmission. It jointly optimizes high‑altitude platform station placement, user association, and power allocation to maximize secrecy spectral efficiency while meeting authentication reliability, power, and coverage constraints. The solution employs a repair‑based cross‑entropy method and a graph‑aware advantage actor‑critic algorithm, achieving up to 135% higher average secrecy spectral efficiency compared to single‑connect transmission.
arXiv:2507. 17188v2 Announce Type: replace-cross Abstract: This paper investigates secure communications in rate-splitting multiple access (RSMA) enabled heterogeneous UAV networks, where multiple UAVs collaboratively serve ground terminals in the presence of eavesdroppers.
The paper introduces SAFA-MZ, a secure adaptive framework for physical‑layer authentication in non‑terrestrial networks. It fuses multiple spatial, angular, combiner, subspace, and Doppler‑delay features into a distributed fingerprint and employs a causal meta‑learning strategy with invariant risk minimization to adapt quickly to new environments. A two‑stage authentication process combines local recognition with selective TDOA localization using a graph attention network, reducing backhaul overhead while achieving 92% accuracy and 96% AUC in simulations.
arXiv:2609.26598v1 Announce Type: cross Abstract: The future 6G networks are expected to incorporate a proliferation of wireless services in diverse environments, which presents a significant challen...
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.
The rapid proliferation of Internet of things (IoT) devices has significantly expanded the cyber-attack surface, necessitating robust and privacy-preserving intrusion detection systems (IDS). However, centralized learning approaches often suffer from severe performance degradation due to high-dimensional traffic data, extreme class imbalance, and highly non-independent and identically distributed (non-IID) data across heterogeneous edge devices.
arXiv:2607. 06115v1 Announce Type: cross Abstract: In next-generation networks, communication systems will no longer be limited to data transmission and will be expected to acquire awareness of the surrounding environment.
arXiv:2607. 04698v1 Announce Type: new Abstract: The rapid proliferation of Internet of things (IoT) devices has significantly expanded the cyber-attack surface, necessitating robust and privacy-preserving intrusion detection systems (IDS).
FedQoS is a federated learning framework that predicts future QoS failure probabilities for candidate access links in dynamic indoor‑outdoor environments, enabling reliable access‑node selection without centralizing user data. Each access node trains locally on its network logs, while a global QoS‑risk predictor is built through federated aggregation. Simulations using physics‑based synthetic datasets show that FedQoS reduces QoS‑failure rates compared to signal‑based and historical‑QoS heuristics, achieving near‑centralized performance even under non‑IID data conditions.
arXiv:2605. 30123v2 Announce Type: replace-cross Abstract: Homomorphic encryption (HE) enables privacy-preserving aggregation in federated learning (FL) by allowing the server to operate on encrypted data without decryption.
The paper presents a channel-informed neural network for physical-layer key generation (PKG) that extracts binary key features directly from IQ measurements while grounding the representation in the multipath channel. The multi-task recurrent network jointly learns reciprocity-preserving features and an auxiliary channel estimate, using deep metric learning and channel-informed supervision. Experiments on indoor and outdoor software-defined-radio data show lower bit disagreement for legitimate users, improved key diversity with ray-traced augmentation, and successful NIST randomness tests after SHA-3 privacy amplification.
arXiv:2605. 21115v2 Announce Type: replace-cross Abstract: Federated learning (FL) has emerged as a promising paradigm for managing electric vehicle (EV) battery data in intelligent transportation systems (ITS), enabling privacy-preserving tasks such as anomaly detection and capacity estimation.
arXiv:2609.13872v1 Announce Type: cross Abstract: As 6G networks transition from theoretical frameworks into operational realities, artificial intelligence (AI) evolves from an add-on optimization to...