基于联邦学习的电商数据协同与精准网络营销研究

Research on Precision Marketing and E-Commerce Data Collaboration via Federated Learning

摘要: 在数字经济背景下，电商平台、商家、支付及物流等多方主体积累了海量互补行为数据，但数据孤岛与隐私约束限制了跨主体协同建模。特别是在精准网络营销场景中，大促高并发的业务需求与传统联邦学习边缘算力受限、通信高延迟及数据高度异构等瓶颈产生严重冲突。为此，本文提出一种面向跨主体协作的轻量级联邦数据协同机制，通过优化云端与边缘侧的协同逻辑，有效降低了商家终端在处理大规模营销数据时的资源负载，并增强了模型对多变消费行为的捕捉能力。实验表明，在模拟大促的高并发环境下，该机制通信量较传统模式降低约94%，可在不挤占核心业务带宽的前提下实现网络营销策略的敏捷迭代，为打破电商数据孤岛提供高效的商业决策支撑。

Abstract: In the digital economy, e-commerce platforms, merchants, payment institutions, and logistics providers accumulate massive complementary data, while data silos and privacy constraints hinder collaborative modeling. In precision marketing scenarios, traditional federated learning suffers from limited edge resources, high communication costs, and heterogeneous data under high-concurrency environments. To address these challenges, this paper proposes a lightweight federated data collaboration mechanism for cross-entity cooperation. By optimizing cloud-edge collaboration, the proposed method reduces terminal resource consumption and enhances the modeling of dynamic consumer behaviors. Experimental results show that under the high-concurrency environment simulating e-commerce large-scale promotions, the proposed mechanism reduces communication overhead by approximately 94% compared with traditional methods, enabling efficient and agile marketing updates without occupying critical business bandwidth and providing efficient commercial decision support for breaking e-commerce data silos.