mlsanityData to 7 Oct 2026

Cali3F: Calibrated Fast Fair Federated Recommendation System

arXiv 2205.13121 ↗First posted 26 May 2022cs.IR cs.CY cs.LGcited by 5

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Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 23 of its 32 references that are in the map. First posted on arXiv 26 May 2022.

Builds on

23 of 32 references.

References · 23

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019Advances and Open Problems in Federated Learningcited by 3,021
  3. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  4. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  5. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,220
  6. 2017Neural Collaborative Filteringcited by 1,108
  7. 2017Federated Multi-Task Learningcited by 669
  8. 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 665
  9. 2020Personalized Federated Learning with Moreau Envelopescited by 612
  10. 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 499
  11. 2019Agnostic Federated Learningcited by 451
  12. 2019Federated Machine Learning: Concept and Applicationscited by 409
11 more
  1. 2019Fair Resource Allocation in Federated Learningcited by 389
  2. 2020Federated learning with hierarchical clustering of local updates to improve training on non-IID datacited by 222
  3. 2020Personalized Federated Learning with First Order Model Optimizationcited by 201
  4. 2019FedHealth: A Federated Transfer Learning Framework for Wearable Healthcarecited by 182
  5. 2020Lower Bounds and Optimal Algorithms for Personalized Federated Learningcited by 122
  6. 2019Secure Federated Matrix Factorizationcited by 102
  7. 2019Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation Systemcited by 79
  8. 2021Federated Neural Collaborative Filteringcited by 30
  9. 2021Stronger Privacy for Federated Collaborative Filtering with Implicit Feedbackcited by 26
  10. 2021A Quantitative Metric for Privacy Leakage in Federated Learningcited by 12
  11. 2021Personalized Federated Learning with Clustering: Non-IID Heart Rate Variability Data Applicationcited by 6

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Cited by year

Citing papers per year: 2023: 2; 2024: 2; 2025: 1.

5 papers.

  1. 2023A Survey on Fairness-aware Recommender Systemscited by 14
  2. 2024Co-clustering for Federated Recommender Systemcited by 9
  3. 2023Fairness-aware Differentially Private Collaborative Filteringcited by 1
  4. 2025Personalized Recommendation Models in Federated Settings: A Surveycited by 1
  5. 2024Towards Fairness in Provably Communication-Efficient Federated Recommender Systemscited by 0