This paper provides a novel approach to analyzing advertising datasets by combining Federated Learning (FL) and Large Language Models (LLMs), and offers a systematic workflow for improving the accuracy of advertising recommendation systems. By adopting a FL paradigm, heterogeneous sources of data collectively train models with shared information, thus allowing distributed and privacy-restricted analysis. At the same time, optimized prompts are engineered for LLMs to decode multidimensional features of advertising information to promote ad personalization and intelligence. The main contribution of this paper is a systematic workflow regarding advertising analysis, including data preprocessing, visualization, federated model training, prompt engineering, and strategic generation. At the stage of data analysis, the FL paradigm, combined with visualization methods, supports presentation of user behavior and advertising performance in a multi-angle manner, allowing model optimization with privacy maintenance. Additionally, prompt designs specific to advertising analysis greatly improve LLM’s interpretability, allowing deep analysis of user interests, advertising trend, as well as ad delivery strategy, ultimately leading to highly personalized ad recommendations. Experimental evidence shows remarkable gains in recommendation accuracy, strategy effectiveness, as well as protection of data privacy. In contrast to previous methods that are based on a centralized model, the workflow suggested has a higher degree of freedom in handling different types of datasets in scale and structure. This approach provides not just a smart, privacy-protected solution to advertising analytics, but also a useful paradigm on applying cross-modal data processing and privacy-protected technology to other fields.

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