Improving the Efficiency of Federated Recommender System

PhD Thesis Proposal Defence


Title: "Improving the Efficiency of Federated Recommender System"

by

Mr. Liu YANG


Abstract:

Recommender system (RecSys) provides personalized recommendation services 
across various applications such as e-commerce, online video, and social 
networking, making considerable profits for companies. The technologies of 
RecSys have evolved from shallow to deep and large models, highlighting the 
transition towards more complex models to better capture user-item 
interactions. However, the centralized nature of traditional RecSys faces 
challenges in data privacy.

Federated recommender systems (FedRec) have emerged as a promising paradigm 
to address the data silo and privacy issues by enabling collaborative model 
training and inference without sharing raw data. However, when combined with 
security-enhancing techniques such as secret sharing or homomorphic 
encryption, FedRec suffers from severe efficiency challenges in terms of 
computation, communication, and convergence. This proposal therefore focuses 
on improving the efficiency of FedRec, systematically studying three 
representative scenarios: (1) horizontal FedRec in cross-device settings 
dominated by shallow models, (2) vertical FedRec in cross-silo settings with 
deep models, and (3) transfer FedRec in client-server settings with large 
models.

To this end, we develop a series of methods tailored to each scenario. For 
horizontal FedRec, we propose FedMMF, which leverages personalized masks and 
adaptive aggregation to reduce the quadratic complexity of 
secret-sharing-based protocols while maintaining accuracy. For vertical 
FedRec, we design PackVFL, an efficient packed homomorphic encryption 
framework that significantly accelerates secure matrix multiplication and 
supports practical deployment with deep models. For transfer FedRec, we 
introduce FedShuffle, a lightweight mechanism to protect user queries to 
large remote models, addressing efficiency bottlenecks of existing 
cryptographic and textual obfuscation methods.

By targeting efficiency as the central problem, this proposal highlights a 
unified perspective across different categories of FedRec, bridging shallow, 
deep, and large models. The proposed methods advance the practicality of 
FedRec and pave the way for scalable, efficient, and privacy-preserving 
recommender systems in real-world applications.


Date:                   Friday, 19 September 2025

Time:                   2:30pm - 4:30pm

Venue:                  Room 2408
                        Lifts 17/18

Committee Members:      Prof. Qiang Yang (Supervisor)
                        Prof. Kai Chen (Supervisor)
                        Prof. Qiong Luo
                        Dr. Binhang Yuan (Chairperson)