arXiv (All)
open access
Toward Realistic Privacy Risk in FL-Enabled OT Intrusion Detection Systems
In Operational Technology (OT) environments, Federated Learning (FL) is increasingly adopted to train Intrusion Detection Systems (IDSs) without sharing raw operational data across facilities. However, model updates may still leak sensitive information about industrial processes, creating privacy ri
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Similarity-Aware Personalized Federated Learning in Heterogeneous Environments#822631
- Server-Enforced Watermarking in U-Shaped Split Federated Learning#1116166
- FLIFRA: Hybrid data poisoning attack detection in federated learning for IoT security#1146720
- PPFL: privacy-preserving federated learning with trusted execution environments#1209236
- Federated Learning-Based Privacy-Preserving Data Aggregation Scheme for IIoT#177321
Record · ID 1341754
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive