In Federated Recommendation (FedRec) systems, communication costs are a
critical bottleneck that arises from the need to transmit neural network models
between user devices and a central server. Prior approaches to these challenges
often lead to issues such as computational overheads, model specificity
constraints, and compatibility issues with secure aggregation protocols. In
response, we propose a novel framework, called Correlated Low-rank Structure
(CoLR), which leverages the concept of adjusting lightweight trainable
parameters while keeping most parameters frozen. Our approach substantially
reduces communication overheads without introducing additional computational
burdens. Critically, our framework remains fully compatible with secure
aggregation protocols, including the robust use of Homomorphic Encryption. Our
approach resulted in a reduction of up to 93.75% in payload size, with only an
approximate 8% decrease in recommendation performance across datasets. Code for
reproducing our experiments can be found at
this https URL

在联邦推荐系统中，通信成本是一个关键瓶颈，该研究提出了一种名为 Correlated Low-rank Structure (CoLR) 的新框架，通过调整轻量级可训练参数并保持大部分参数冻结，大幅降低通信开销并与安全聚合协议兼容。该方法在数据集范围内减少了高达 93.75% 的负载大小，仅导致推荐性能下降约 8%。