paperSeptember 2026Unreviewed

Privacy Leakage in Federated Learning: Gradient-Based Client Identity Inference and Defenses for Inertial Sensing in Vehicular Edge Networks

Ali Akarma, Toqeer Ali Syed, Muhammad Khan, Qurat-ul-ain Mastoi, Adeel Ahmad

Abstract

As vehicular networks move toward 5G/6G edge intelligence, federated learning (FL) is widely promoted as a privacy-preserving way for vehicles and infrastructure to train shared models without exposing raw sensor data. Yet the updates clients transmit still leak enough information to identify who sent them, which threatens the anonymity that safety-critical V2X applications assume and adds to existing concerns over adversarial ML, model poisoning, and backdoor attacks. We study server-side clien

Framework mappings

OWASP Top 10 for LLM Applications

LLM02Sensitive Information Disclosure
LLM04Data and Model Poisoning

MITRE ATLAS

AML.T0020Poison Training Data
AML.T0024.000Infer Training Data Membership

Suggested from the entry's categories.

Cite

@misc{akarma2026privacy,
  title = {{Privacy Leakage in Federated Learning: Gradient-Based Client Identity Inference and Defenses for Inertial Sensing in Vehicular Edge Networks}},
  author = {Ali Akarma and Toqeer Ali Syed and Muhammad Khan and Qurat-ul-ain Mastoi and Adeel Ahmad},
  year = {2026},
  month = sep,
  eprint = {2609.02971},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2609.02971}
}