Privacy-Preserving Topology-Guided Safety for LLM-Based Multi-Agent Systems via Federated Graph Learning

This paper presents a federated graph learning approach to ensure safety in multi-agent LLM systems while preserving privacy across distributed participants. The method uses topology-guided constraints to maintain safety guarantees without centralizing sensitive model or organizational data, addressing a key challenge for regulated industries deploying collaborative AI systems.

This paper presents a federated graph learning approach to ensure safety in multi-agent LLM systems while preserving privacy across distributed participants. The method uses topology-guided constraints to maintain safety guarantees without centralizing sensitive model or organizational data, addressing a key challenge for regulated industries deploying collaborative AI systems.

High-quality data pipelines remain the most consistent bottleneck in enterprise AI maturity. QuettaMinds helps clients close the gap between raw data assets and production-ready AI inputs.