Fairness-aware Federated Matrix Factorization
Abstract
Achieving fairness over different user groups in recommender systems is an important problem. The majority of existing works achieve fairness through constrained optimization that combines the recommendation loss and the fairness constraint. To achieve fairness, the algorithm usually needs to know each user’s group affiliation feature such as gender or race. However, such involved user group feature is usually sensitive and requires protection. In this work, we seek a federated learning solution for the fair recommendation problem and identify the main challenge as an algorithmic conflict between the global fairness objective and the localized federated optimization process. On one hand, the fairness objective usually requires access to all users’ group information. On the other hand, the federated learning systems restrain the personal data in each user’s local space. As a resolution, we propose to communicate group statistics during federated optimization and use differential privacy techniques to avoid exposure of users’ group information when users require privacy protection. We illustrate the theoretical bounds of the noisy signal used in our method that aims to enforce privacy without overwhelming the aggregated statistics. Empirical results show that federated learning may naturally improve user group fairness and the proposed framework can effectively control this fairness with low communication overheads.
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Fairness-aware performance metrics provide a holistic view of fairness model performance.
“Provides a holistic view of fairness model performance”
From AI Fairness in Data Management and Analytics: A Review on Challenges, Methodologies and Applications (2023) · cites this paper as [259]
The choice of fairness metric may not fully capture desired notions of fairness.
“Choice of fairness metric desired notions of fairness may not fully capture”
From AI Fairness in Data Management and Analytics: A Review on Challenges, Methodologies and Applications (2023) · cites this paper as [259]
Fairness-aware performance metrics simultaneously evaluate accuracy and fairness.
“Simultaneously evaluates accuracy and fairness”
From AI Fairness in Data Management and Analytics: A Review on Challenges, Methodologies and Applications (2023) · cites this paper as [259]
Cites
Differential Privacy: A Survey of Results
Dwork, C. (2008) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-79228-4_1 · Cited by 3,542 · see citing works
Justifying Recommendations using Distantly-Labeled Reviews and Fine-Grained Aspects
Ni, J., Li, J., & McAuley, J. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1018 · Cited by 1,283 · see citing works
Algorithmic Decision Making and the Cost of Fairness
Corbett-Davies, S., Pierson, E., Feller, A., Goel, S., & Huq, A. (2017) · Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/3097983.3098095 · Cited by 1,149 · see citing works
Novelty and diversity in information retrieval evaluation
Clarke, C. L. A., Kolla, M., Cormack, G. V., Olga Vechtomova, A., Ashkan, A., Büttcher, S., & MacKinnon, I. (2008) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/1390334.1390446 · Cited by 968 · see citing works
Fairness of Exposure in Rankings
Singh, A., & Joachims, T. (2018) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3219819.3220088 · Cited by 607 · see citing works
(2022) · Springer eBooks · doi:10.1007/978-1-0716-2197-4 · Cited by 587 · see citing works
Secure Federated Matrix Factorization
Chai, D., Wang, L., Chen, K., & Yang, Q. (2020) · IEEE Intelligent Systems · doi:10.1109/mis.2020.3014880 · Cited by 473 · see citing works
Controlling Popularity Bias in Learning-to-Rank Recommendation
Abdollahpouri, H., Burke, R., & Mobasher, B. (2017) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/3109859.3109912 · Cited by 437 · see citing works
Fairness in Recommendation Ranking through Pairwise Comparisons
Beutel, A., Chen, J., Doshi, T. S., Qian, H., Wei, L., Wu, Y., Heldt, L., Zhao, Z., Hong, L., Ed, H., et al. (2019) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3292500.3330745 · Cited by 374 · see citing works
Measure Contribution of Participants in Federated Learning
Wang, G., Dang, C. X., & Zhou, Z. Y. (2019) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata47090.2019.9006179 · Cited by 243 · see citing works
User-oriented Fairness in Recommendation
Li, Y., Chen, H., Fu, Z., Ge, Y., & Zhang, Y. (2021) · International World Wide Web Conference (WWW) · doi:10.1145/3442381.3449866 · Cited by 225 · see citing works
Self-citationFairness-Aware Explainable Recommendation over Knowledge Graphs
Fu, Z., Xian, Y., Gao, R., Zhao, J., Huang, Q., Ge, Y., Xu, S., Geng, S., Shah, C. S., Zhang, Y., et al. (2020) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3397271.3401051 · Cited by 213 · see citing works
Self-citationTowards Long-term Fairness in Recommendation
Ge, Y., Liu, S., Gao, R., Xian, Y., Li, Y., Zhao, X., Pei, C., Sun, F., Ge, J., Ou, W., et al. (2021) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3437963.3441824 · Cited by 201 · see citing works
Self-citationInvestigating the Impact of Gender on Rank in Resume Search Engines
Chen, L., Ma, R., Hannák, A., & Wilson, C. (2018) · Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3174225 · Cited by 155 · see citing works
Towards Personalized Fairness based on Causal Notion
Li, Y., Chen, H., Xu, S., Ge, Y., & Zhang, Y. (2021) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3404835.3462966 · Cited by 145 · see citing works
Self-citationAn algorithm for efficient privacy-preserving item-based collaborative filtering
Li, D., Chen, C., Lv, Q., Shang, L., Zhao, Y., Lu, T., & Gu, N. (2014) · Future Generation Computer Systems · doi:10.1016/j.future.2014.11.003 · Cited by 121 · see citing works
Peer-to-peer based recommendations for mobile commerce
Tveit, A. (2001) · doi:10.1145/381461.381466 · Cited by 99 · see citing works
Personalized Privacy-Preserving Social Recommendation
Meng, X., Wang, S., Shu, K., Li, J., Chen, B., Liu, H., & Zhang, Y. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11714 · Cited by 84 · see citing works
Self-citationToward Pareto Efficient Fairness-Utility Trade-off in Recommendation through Reinforcement Learning
Ge, Y., Zhao, X., Yu, L., Paul, S., Hu, D. J., Hsieh, C.-C., & Zhang, Y. (2022) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3488560.3498487 · Cited by 77 · see citing works
Self-citationA Simple and Efficient Federated Recommender System
Jalalirad, A., Scavuzzo, M., Capota, C., & Sprague, M. (2019) · IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT) · doi:10.1145/3365109.3368788 · Cited by 75 · see citing works
Controlling Polarization in Personalization
Celis, L. E., Kapoor, S., Salehi, F., & Vishnoi, N. K. (2019) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3287560.3287601 · Cited by 65 · see citing works
Li, M., Liu, Z., Smola, A. J., & Wang, Y. (2016) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/2835776.2835781 · Cited by 47 · see citing works
Towards Fair Federated Learning
Zhou, Z., Chu, L., Liu, C., Wang, L., Pei, J., & Zhang, Y. (2021) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3447548.3470814 · Cited by 47 · see citing works
Self-citationTutorial on Fairness of Machine Learning in Recommender Systems
Li, Y., Ge, Y., & Zhang, Y. (2021) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3404835.3462814 · Cited by 46 · see citing works
Self-citationDeepFair: Deep Learning for Improving Fairness in Recommender Systems.
Bobadilla, J., Cabrera, R. L., González-Prieto, Á., & Ortega, F. (2021) · International Journal of Interactive Multimedia and Artificial Intelligence · doi:10.9781/ijimai.2020.11.001 · Cited by 40 · see citing works
Duriakova, E., Τράγος, Η., Smyth, B., Hurley, N., Peña, F. J., Symeonidis, P., Geraci, J. R., & Lawlor, A. (2019) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/3298689.3347035 · Cited by 39 · see citing works
Maximizing Marginal Fairness for Dynamic Learning to Rank
Yang, T., & Ai, Q. (2021) · International World Wide Web Conference (WWW) · doi:10.1145/3442381.3449901 · Cited by 36 · see citing works
Addressing Bias and Fairness in Search Systems
Gao, R., & Shah, C. S. (2021) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3404835.3462807 · Cited by 22 · see citing works
Flagged first, then by citations
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AI Fairness in Data Management and Analytics: A Review on Challenges, Methodologies and Applications
Chen, P., Wu, L., & Wang, L. (2023) · Applied Sciences · doi:10.3390/app131810258 · Cited by 155 · see citing works
Emerging trends in federated learning: from model fusion to federated X learning
Ji, S., Tan, Y., Saravirta, T., Yang, Z. Q., Liu, Y., Vasankari, L., Pan, S., Long, G., & Walid, A. (2024) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-024-02119-1 · Cited by 171 · see citing works
Recent Developments in Recommender Systems: A Survey [Review Article]
Li, Y., Liu, K., Satapathy, R., Wang, S., & Cambria, E. (2024) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2024.3363984 · Cited by 120 · see citing works
A Survey on Trustworthy Recommender Systems
Ge, Y., Liu, S., Fu, Z., Tan, J., Li, Z., Xu, S., Li, Y., Xian, Y., & Zhang, Y. (2024) · ACM Transactions on Recommender Systems · doi:10.1145/3652891 · Cited by 144 · see citing works
Self-citationFairness and privacy preserving in federated learning: A survey
Rafi, T. H., Noor, F. A., Hussain, T., & Chae, D. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.102198 · Cited by 131 · see citing works
Dual Personalization on Federated Recommendation
Zhang, C., Long, G., Zhou, T., Yan, P., Zhang, Z., Zhang, C., & Yang, B. (2023) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2023/507 · Cited by 77 · see citing works
Ali, W., Zhou, X., & Shao, J. (2024) · ACM Computing Surveys · doi:10.1145/3708982 · Cited by 50 · see citing works
No Prejudice! Fair Federated Graph Neural Networks for Personalized Recommendation
Agrawal, N., Sirohi, A. K., Kumar, S., & Jayadeva (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i10.28950 · Cited by 34 · see citing works
Towards fair and personalized federated recommendation
Wang, S., Tao, H., Li, J., Ji, X., Gao, Y., & Gong, M. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.110234 · Cited by 31 · see citing works
On-Device Recommender Systems: A Comprehensive Survey
Yin, H., Qu, L., Chen, T., Yuan, W., Zheng, R., Long, J., Xia, X., Shi, Y., & Chengqi Zhang, B. (2025) · Data Science and Engineering · doi:10.1007/s41019-025-00308-8 · Cited by 67 · see citing works
Recent advances and future challenges in federated recommender systems
Harasic, M., Keese, F.-S., Mattern, D., & Paschke, A. (2023) · International Journal of Data Science and Analytics · doi:10.1007/s41060-023-00442-4 · Cited by 32 · see citing works
Causal Inference for Recommendation: Foundations, Methods, and Applications
Xu, S., Ji, J., Li, Y., Ge, Y., Tan, J., & Zhang, Y. (2025) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3714430 · Cited by 38 · see citing works
Self-citationTowards Efficient Communication and Secure Federated Recommendation System via Low-rank Training
Nguyen, N.-H., Nguyen, T.-A., Nguyen, T. A., Hoang, V. T., Le, D. D., & Wong, K. (2024) · International World Wide Web Conference (WWW) · doi:10.1145/3589334.3645702 · Cited by 25 · see citing works
SFFL: Self-aware fairness federated learning framework for heterogeneous data distributions
Zhang, J., Li, Y., Wu, D., Zhao, Y., & Shivakumara, P. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.126418 · Cited by 14 · see citing works
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Gupta, S., Kaur, K., & Jain, S. (2023) · Journal of Intelligent Information Systems · doi:10.1007/s10844-023-00817-w · Cited by 14 · see citing works
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Yang, E., Xiong, Y., Yuan, W., Pan, W., Yang, Q., & Ming, Z. (2025) · Science China Information Sciences · doi:10.1007/s11432-024-4310-7 · Cited by 12 · see citing works
Explainability, Robustness, and Fairness in User-Centric Intelligent Systems: A Systematic Review
Zahid, I. A., Garfan, S., Chyad, M. A., Albahri, A. S., Albahri, O. S., Alamoodi, A. H., Deveci, M., Homod, R. Z., & Alzubaidi, L. (2025) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2025.3567604 · Cited by 11 · see citing works
Personalized Recommendation Models in Federated Settings: A Survey
Zhang, C., Long, G., Zhang, Z., Li, Z., Zhang, H., Yang, Q., & Yang, B. (2025) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2025.3606643 · Cited by 14 · see citing works
Collaborative filtering recommendations based on multi-factor random walks
Guo, L., Luan, K., Sun, L., Luo, Y., & Zheng, X. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106409 · Cited by 8 · see citing works
Recommender System: A Comprehensive Overview of Technical Challenges and Social Implications
An, Y., Tan, Y., Sun, X. J., & Ferrari, G. (2024) · ICCK Transactions on Sensing Communication and Control · doi:10.62762/tscc.2024.898503 · Cited by 8 · see citing works
Leave No One Behind: Fairness-Aware Cross-Domain Recommender Systems for Non-Overlapping Users
Chen, W., Zhao, Y., Chen, L., & Pan, W. (2025) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/3705328.3748082 · Cited by 6 · see citing works
Algorithmic fairness in social context
Huang, Y., Liu, W., Gao, W., Lu, X., Liang, X., Yang, Z., Li, H., Ma, L., & Tang, S. (2023) · BenchCouncil Transactions on Benchmarks Standards and Evaluations · doi:10.1016/j.tbench.2023.100137 · Cited by 6 · see citing works
BFRecSys: A Blockchain-based Federated Matrix Factorization for Recommendation Systems
Hou, D., & Zhang, J. (2023) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata59044.2023.10386918 · Cited by 6 · see citing works
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Yang, Z., Ge, Y., Su, C., Wang, D., Zhao, X., & Ying, Y. (2023) · Companion Proceedings of the ACM Web Conference 2023 · doi:10.1145/3543873.3587577 · Cited by 6 · see citing works
Self-citationZhang, S., Ding, Y., Tang, W., Sun, W., Liao, Y., & Zhou, P. (2025) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3701551.3703513 · Cited by 4 · see citing works
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Wang, S., Du, W., Bhuiyan, A., & Chen, Z. (2025) · Computational Intelligence · doi:10.1111/coin.70024 · Cited by 3 · see citing works
Chen, G., Li, C., Du, F., Yuan, X., Chi, C., Yin, Z., Wang, B., Li, T., Bao, X., & Wang, W. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3482703 · Cited by 2 · see citing works
GCPA: GAN-Based Collusive Poisoning Attack in Federated Recommender Systems
Gu, T., TAN, S., Hao, F., Liu, X., Chang, L., & Liu, Y. (2025) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2025.3579807 · Cited by 2 · see citing works
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Zhang, G., Zhang, X., Qi, L., Xu, X., Li, M., & Dou, W. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-5562-2_10 · Cited by 2 · see citing works
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Lee, C., Huang, S.-H., & Chen, C.-T. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-6400-9_22 · Cited by 1 · see citing works
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Nguyen, H. (2026) · Journal of Intelligent Information Systems · doi:10.1007/s10844-026-01035-w · Cited by 1 · see citing works
Zheng, D., Wang, L., Feng, J., Liao, X., Xiao, N., Tian, Y., & Liao, Q. (2026) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2026.3678755 · Cited by 1 · see citing works
Wu, D., Zhong, S., He, Y., Luo, X., & Gao, X. (2026) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2026.3683950 · Cited by 2 · see citing works
Sajjad, F., Tahir, R. M., Yan, W., Wei, Z., & Zhou, Y. X. (2026) · International Journal of Data Science and Analytics · doi:10.1007/s41060-026-01173-y · Cited by 1 · see citing works
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Wang, H. (2024) · Frontiers in artificial intelligence and applications · doi:10.3233/faia231233 · Cited by 1 · see citing works
Santos, R. V. M., & Comarela, G. (2024) · Anais do Brazilian Workshop on Social Network Analysis and Mining (BraSNAM) · doi:10.5753/brasnam.2024.1875
Zhong, S., Tang, Z., & Wu, S. (2024) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc54092.2024.10832043
dos Santos, R. V. M., & Comarela, G. (2025) · Journal on Interactive Systems · doi:10.5753/jis.2025.5407
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Hu, M., Li, Y., Liao, P., Zhou, Y., & Wu, D. (2025) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2025.3630641
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Kassab, L., George, E., Needell, D. M., Geng, H., Nia, N. J., & Li, A. X. (2026) · Frontiers in Big Data · doi:10.3389/fdata.2026.1737043 · Cited by 1 · see citing works
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Alshammari, M. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6929303
An adaptive scalarization framework for multi-objective recommender systems
Zaizi, F. E., Qassimi, S., & Rakrak, S. (2026) · Knowledge and Information Systems · doi:10.1007/s10115-026-02828-9
Zhao, Y., Chen, W., Chen, L., & Pan, W. (2026) · ACM Transactions on Recommender Systems · doi:10.1145/3841183
Roy, F., Ding, X., Choo, K. K. R., & Zhou, P. (2024) · arXiv · doi:10.48550/arxiv.2409.12651
- Authors
- Liu, S., Ge, Y., Xu, S., Zhang, Y., & Marian, A.
- Affiliations
- Rutgers, The State University of New Jersey (US)
- Computer Science, Rutgers University, United States
- Venue
- Proceedings of the 16th ACM Conference on Recommender Systems
- Publisher
- ACM
- Published
- 2022-09-13
- Pages
- 168-178
- OpenAlex
- W4296591849
- Cited by
- 43
- References
- 28
- Citations by year
- 2026: 8 · 2025: 13 · 2024: 12 · 2023: 10
- FWCI
- 4.84
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM Conference on Recommender Systems (RecSys) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- federated learning, recommendation fairness, recommender systems, matrix factorization, differential privacy, communication overhead
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