Federated Cross-Client Collaborative Filtering with Tensor Compressive Learning

Abstract

Federated collaborative filtering enables privacy-preserving recommendation systems but faces challenges in capturing high-order interactions, reducing communication overhead, and minimizing accuracy degradation. To address these issues, we propose Federated Compressive Collaborative Filtering (FCCF), a novel framework that leverages tensor compressive learning for cross-client predictions. FCCF employs a tensor-based model with GNN-based extraction to efficiently represent multi-type item and dual-role user nodes and introduces a sketch-to-embedding projection for feature analysis. It performs inference directly on compressed data, eliminating the need for precise signal reconstruction, and employs client-specific sampling matrices along with regularization to enhance privacy and preserve local representations. Experiments demonstrate FCCF’s effectiveness in improving prediction accuracy, robustness to privacy noise, and communication efficiency.