Abstract: Existing click-through rate (CTR) prediction methods typically rely on centralized data storage and modeling. However, due to high user privacy sensitivity and data protection regulations, user behavior data across different platforms cannot be directly shared or aggregated. At the same time, most CTR prediction models adopt deep architectures with large parameter scales, leading to high communication and computation costs that limit their practical application. To address these problems, this paper proposes an efficient Federated Recommendation System (FedRSS) based on a Slim Module and a Salience Awareness Module. FedRSS aggregates cross-platform feature representations within a federated learning framework while preserving privacy. The Slim Module replaces the traditional Hadamard product with an inner product to reduce model complexity and stacks compression layers to decrease parameters, while the Saliency-Aware Module employs a bit-level attention mechanism to dynamically assign feature weights and enhance the modeling of important features. In addition, FedRSS introduces a local differential privacy mechanism to further protect user information. Experiments on three public datasets, Criteo, Avazu, and MovieLens, show that FedRSS achieves notable improvements in both performance and efficiency, with RelaImpr increases of 11.04%, 3.38%, and 4.82%, respectively, and significantly reduced training time. The results demonstrate that FedRSS achieves efficient CTR prediction under privacy constraints and provides a promising direction for developing low-overhead federated recommendation systems.

摘要： 现有的点击率预测（Click-through Rate,CTR)方法通常依赖于集中式的数据存储与建模方式，但由于用户隐私敏感性高以及数据保护法规的限制，不同平台间的用户行为数据难以直接共享与聚合。同时，主流的CTR预测模型往往采用参数规模庞大的深度结构，导致通信与计算开销过大，限制了模型的实际应用。为解决上述问题，本文提出了一种基于精简模块和显著性感知模块的高效联邦推荐系统(Federated Recommendation System ,FedRSS)。该方法通过联邦学习框架在保护隐私的前提下实现跨平台特征表示的聚合，其中，精简模块利用内积替换传统的哈达玛积以降低模型复杂度，并通过堆叠压缩层减少参数数量。显著性感知模块基于位级注意力机制动态分配特征权重，从而增强对关键特征的建模能力。此外，FedRSS引入本地差分隐私机制，以进一步防止用户隐私泄露。基于Criteo、Avazu和MovieLens三个公开数据集的大量实验结果表明，FedRSS在保证隐私安全的同时实现了显著的性能与效率提升，其中RelaImpr指标分别提高了11.04%、3.38%和4.82%，训练时间明显缩短。研究结果表明，本文提出的FedRSS不仅能够在隐私保护条件下实现高效的CTR预测，还为构建低开销的联邦推荐系统提供了新的思路。