LAFA: Agentic LLM-Driven Federated Analytics over Decentralized Data Sources

Abstract--Large Language Models (LLMs) have shown great promise in automating data analytics tasks by interpreting natural language queries and generating multi-operation execution plans. However, existing LLM-agent-based analytics frameworks operate under the assumption of centralized data access, offering little to no privacy protection. In contrast, federated analytics (F A) enables privacy-preserving computation across distributed data sources, but lacks support for natural language input and requires structured, machine-readable queries. In this work, we present LAF A, the first system that integrates LLM-agent-based data analytics with F A. LAF A introduces a hierarchical multi-agent architecture that accepts natural language queries and transforms them into optimized, executable F A workflows. T o improve execution efficiency, an optimizer agent rewrites and merges multiple DAGs, eliminating redundant operations and minimizing computational and communicational overhead. Our experiments demonstrate that LAF A consistently outperforms baseline prompting strategies by achieving higher execution plan success rates and reducing resource-intensive F A operations by a substantial margin. This work establishes a practical foundation for privacy-preserving, LLM-driven analytics that supports natural language input in the F A setting. The rapid development of Large Language Models (LLMs) has offered unprecedented capabilities in natural language understanding, reasoning, and planning [1], significantly transforming the landscape of data analytics. LLMs can interpret complex analytical intents, generate structured code, and orchestrate multi-step tasks by interacting with external environments such as databases and computation sandboxes. These capabilities have led to the emergence of LLM-based agents that decompose high-level queries, plan analytical workflows, and execute or verify results through tool interactions.