Cali3F: Calibrated Fast Fair Federated Recommendation System
Zhitao Zhu, Shijing Si, Jianzong Wang, Jing Xiao

TL;DR
Cali3F is a federated recommendation framework that enhances fairness and convergence speed by calibrating local models and employing clustering-based aggregation, demonstrated effectively on benchmark datasets.
Contribution
The paper introduces Cali3F, a novel federated recommendation system that improves fairness and training speed through calibration and clustering techniques.
Findings
Cali3F significantly improves recommendation fairness.
Cali3F accelerates training convergence.
Cali3F outperforms traditional aggregation methods.
Abstract
The increasingly stringent regulations on privacy protection have sparked interest in federated learning. As a distributed machine learning framework, it bridges isolated data islands by training a global model over devices while keeping data localized. Specific to recommendation systems, many federated recommendation algorithms have been proposed to realize the privacy-preserving collaborative recommendation. However, several constraints remain largely unexplored. One big concern is how to ensure fairness between participants of federated learning, that is, to maintain the uniformity of recommendation performance across devices. On the other hand, due to data heterogeneity and limited networks, additional challenges occur in the convergence speed. To address these problems, in this paper, we first propose a personalized federated recommendation system training algorithm to improve the…
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Taxonomy
TopicsPrivacy-Preserving Technologies in Data · Stochastic Gradient Optimization Techniques · Recommender Systems and Techniques
