mlsanityData to 7 Oct 2026

Dubhe: Towards Data Unbiasedness with Homomorphic Encryption in Federated Learning Client Selection

arXiv 2109.04253 ↗First posted 8 Sep 2021cs.CR cs.DC cs.LGcited by 7

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Abstract (arXiv)

Federated learning (FL) is a distributed machine learning paradigm that allows clients to collaboratively train a model over their own local data. FL promises the privacy of clients and its security can be strengthened by cryptographic methods such as additively homomorphic encryption (HE). However, the efficiency of FL could seriously suffer from the statistical heterogeneity in both the data distribution discrepancy among clients and the global distribution skewness. We mathematically demonstrate the cause of performance degradation in FL and examine the performance of FL over various datasets. To tackle the statistical heterogeneity problem, we propose a pluggable system-level client selection method named Dubhe, which allows clients to proactively participate in training, meanwhile preserving their privacy with the assistance of HE. Experimental results show that Dubhe is comparable with the optimal greedy method on the classification accuracy, with negligible encryption and communication overhead.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 23 of its 36 references that are in the map. First posted on arXiv 8 Sep 2021.

Builds on

23 of 36 references.

References · 23

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  3. 2019Class-Balanced Loss Based on Effective Number of Samplescited by 1,239
  4. 2018Federated Learning with Non-IID Datacited by 1,099
  5. 2020Adaptive Federated Optimizationcited by 1,036
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  7. 2018LEAF: A Benchmark for Federated Settingscited by 780
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  10. 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 347
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  12. 2019The Non-IID Data Quagmire of Decentralized Machine Learningcited by 303
11 more
  1. 2020FedML: A Research Library and Benchmark for Federated Machine Learningcited by 298
  2. 2018Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learningcited by 242
  3. 2018A Hybrid Approach to Privacy-Preserving Federated Learningcited by 208
  4. 2020Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategiescited by 196
  5. 2017Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryptioncited by 174
  6. 2019Federated Learning for Wireless Communications: Motivation, Opportunities and Challengescited by 128
  7. 2020Federated Visual Classification with Real-World Data Distributioncited by 126
  8. 2016An Analysis of Deep Neural Network Models for Practical Applicationscited by 114
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Cited by year

Citing papers per year: 2023: 4; 2024: 1; 2025: 2.

7 papers.

  1. 2023Over-The-Air Federated Learning: Status Quo, Open Challenges, and Future Directionscited by 21
  2. 2023A Survey on Class Imbalance in Federated Learningcited by 12
  3. 2024A Comprehensive Survey of Federated Transfer Learning: Challenges, Methods and Applicationscited by 8
  4. 2023A Systematic Literature Review on Client Selection in Federated Learningcited by 3
  5. 2023User Assignment and Resource Allocation for Hierarchical Federated Learning over Wireless Networkscited by 1
  6. 2025FedWCM: Unleashing the Potential of Momentum-based Federated Learning in Long-Tailed Scenarioscited by 0
  7. 2025SenseCrypt: Sensitivity-guided Selective Homomorphic Encryption for Joint Federated Learning in Cross-Device Scenarioscited by 0