TraceFed: Federated Safety Intervention for Distributed Large Models with Privacy-Preserving Reasoning Monitoring
Keywords:
federated safety; large foundation models; privacy-preserving monitoring; distributed AI governance; path-level intervention; secure aggregation; robust AI systemsAbstract
The deployment of large foundation models in distributed, privacy-sensitive environments introduces unprecedented challenges for safety monitoring and intervention. While centralized safety mechanisms exist, they conflict with the distributed nature of modern machine learning infrastructures, where data and model updates reside across multiple administrative domains. This paper proposes TraceFed, a federated safety intervention framework that enables coordinated, privacy-preserving reasoning monitoring across distributed large models. TraceFed integrates cryptographic secure aggregation with path-level intervention strategies to detect and mitigate harmful outputs without exposing raw model internals or user data. The architecture leverages a federation of safety authorities that collectively maintain a global safety policy while respecting local autonomy. We examine structural trade-offs between intervention granularity, communication overhead, and privacy guarantees, and discuss governance models that balance centralized oversight with decentralized execution. The paper also addresses robustness and fairness challenges, including adversarial attacks on the monitoring infrastructure and distributional biases in safety alerts. Deployment considerations such as latency constraints, auditability, and energy sustainability are analyzed within the context of real-world large-scale systems. Policy implications for regulatory compliance and cross-jurisdictional accountability are explored. Through this work, we aim to establish a foundational framework for safe, privacy-preserving operation of distributed large models, bridging the gap between federated learning principles and modern AI safety research.
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