Federated LLMs for Personalized CRM Automation: Privacy-Preserving Customer Insights Across Multi-Cloud Platforms

Abstract

Customer Relationship Management (CRM) systems increasingly rely on artificial intelligence to generate insights, automate campaigns, and personalize user experiences. However, deploying large language models (LLMs) across multicloud infrastructures introduces significant challenges in data privacy, interoperability, and model governance. This paper presents a federated LLM framework for CRM automation that enables organizations to collaboratively train models without sharing sensitive customer data. The approach integrates cross-cloud federated orchestration, encrypted communication pipelines, and domain-adaptive personalization modules for region-specific customer behavior modeling. Experiments conducted across heterogeneous datasets illustrate the scalability of framework, accuracy, and robustness, achieving low latency, strong differential privacy guarantees, and efficient cloud-edge deployment. The proposed system bridges enterprise-scale data analytics and privacy-preserving generative intelligence, setting a foundation for responsible AI-driven CRM ecosystems.