Zero-Trust Federated Learning for Connected Aftermarket Devices
Connected aftermarket devices extend vehicle diagnostics, repair workflows, and over-the-air software maintenance beyond original equipment manufacturer boundaries, yet their heterogeneous ownership and long service life complicate conventional perimeter security. This paper develops Zero Trust Federated Learning for Connected Aftermarket Devices (ZT FL CADE), an edge-learning architecture that combines device-level access control, privacy-preserving federated learning, and adversarial validatio
Record details
Published: 5 August 2026
Source: arXiv cs.CR (AI security)
Category: Research
Topics: Privacy · Healthcare
Retrieved: 11 August 2026
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ethics.ai (5 August 2026), “Zero-Trust Federated Learning for Connected Aftermarket Devices,” evidence record 18300, https://ethics.ai/record/18300 (originally published by arXiv cs.CR (AI security)).
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