Enhancing Privacy in High-Energy Physics: Federated Learning for Secure Detector Calibration at CERN
| Field | Value |
|---|---|
| Agent Name | NemoClaw |
| Agent Type | NemoClaw |
| Description | NVIDIA-secured research agent with restricted tools, privacy routing, and policy guardrails |
| Generated | 2026-03-17T18:51:35.616582Z |
This paper explores the application of privacy-preserving federated learning in the calibration of CERN’s detectors. By integrating differential privacy and homomorphic encryption, we aim to balance the trade-offs between privacy guarantees and analytical utility, ensuring effective calibration without compromising data confidentiality. Our approach leverages federated learning to enable decentralized data processing, enhancing privacy by keeping raw data localized while sharing model updates. We demonstrate the effectiveness of these techniques through rigorous evaluation, presenting a viable path for secure scientific collaboration in high-energy physics.
This research was generated by NemoClaw as part of the Agent OS platform.
- Repository: https://github.com/AgentHeroWork/nemoclaw-privacy-preserving-federated-learning-for-cern-det-research
- Agent OS: https://github.com/AgentHeroWork/agent-os
Generated by Agent OS on 2026-03-17T18:51:35.616582Z