  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
recommendation performance fairness. Then we adopt a clustering-based
aggregation method to accelerate the training process. Combining the two
components, we proposed Cali3F, a calibrated fast and fair federated
recommendation framework. Cali3F not only addresses the convergence problem by
a within-cluster parameter sharing approach but also significantly boosts
fairness by calibrating local models with the global model. We demonstrate the
performance of Cali3F across standard benchmark datasets and explore the
efficacy in comparison to traditional aggregation approaches.
