mlsanityData to 14 Oct 2026

Can One Safety Loop Guard Them All? Agentic Guard Rails for Federated Computing

arXiv 2506.20000 ↗First posted 24 Jun 2025cs.CR cs.DC cs.LGcited by 0

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Abstract (arXiv)

We propose Guardian-FC, a novel two-layer framework for privacy preserving federated computing that unifies safety enforcement across diverse privacy preserving mechanisms, including cryptographic back-ends like fully homomorphic encryption (FHE) and multiparty computation (MPC), as well as statistical techniques such as differential privacy (DP). Guardian-FC decouples guard-rails from privacy mechanisms by executing plug-ins (modular computation units), written in a backend-neutral, domain-specific language (DSL) designed specifically for federated computing workflows and interchangeable Execution Providers (EPs), which implement DSL operations for various privacy back-ends. An Agentic-AI control plane enforces a finite-state safety loop through signed telemetry and commands, ensuring consistent risk management and auditability. The manifest-centric design supports fail-fast job admission and seamless extensibility to new privacy back-ends. We present qualitative scenarios illustrating backend-agnostic safety and a formal model foundation for verification. Finally, we outline a research agenda inviting the community to advance adaptive guard-rail tuning, multi-backend composition, DSL specification development, implementation, and compiler extensibility alongside human-override usability.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 4 of its 26 references that are in the map. First posted on arXiv 24 Jun 2025.

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4 of 26 references.

References · 4

  1. 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,592
  2. 2023SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Modelscited by 810
  3. 2021Opacus: User-Friendly Differential Privacy Library in PyTorchcited by 383
  4. 2024A Multiparty Homomorphic Encryption Approach to Confidential Federated Kaplan Meier Survival Analysiscited by 2

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