mlsanityData to 13 Oct 2026

DReS-FL: Dropout-Resilient Secure Federated Learning for Non-IID Clients via Secret Data Sharing

arXiv 2210.02680 ↗First posted 6 Oct 2022cs.LG cs.CRcited by 17

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Abstract (arXiv)

Federated learning (FL) strives to enable collaborative training of machine learning models without centrally collecting clients' private data. Different from centralized training, the local datasets across clients in FL are non-independent and identically distributed (non-IID). In addition, the data-owning clients may drop out of the training process arbitrarily. These characteristics will significantly degrade the training performance. This paper proposes a Dropout-Resilient Secure Federated Learning (DReS-FL) framework based on Lagrange coded computing (LCC) to tackle both the non-IID and dropout problems. The key idea is to utilize Lagrange coding to secretly share the private datasets among clients so that each client receives an encoded version of the global dataset, and the local gradient computation over this dataset is unbiased. To correctly decode the gradient at the server, the gradient function has to be a polynomial in a finite field, and thus we construct polynomial integer neural networks (PINNs) to enable our framework. Theoretical analysis shows that DReS-FL is resilient to client dropouts and provides privacy protection for the local datasets. Furthermore, we experimentally demonstrate that DReS-FL consistently leads to significant performance gains over baseline methods.

Key facts. Cited by 17 papers in the map (data through 13 Oct 2026). Builds on 29 of its 60 references that are in the map. First posted on arXiv 6 Oct 2022.

Builds on

29 of 60 references.

References · 29

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,653
  2. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,328
  3. 2019Advances and Open Problems in Federated Learningcited by 3,040
  4. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,273
  5. 2018Federated Learning with Non-IID Datacited by 1,106
  6. 2021Model-Contrastive Federated Learningcited by 766
  7. 2020Inverting Gradients -- How easy is it to break privacy in federated learning?cited by 711
  8. 2021Federated Learning on Non-IID Data Silos: An Experimental Studycited by 568
  9. 2021Federated Learning Based on Dynamic Regularizationcited by 559
  10. 2021Data-Free Knowledge Distillation for Heterogeneous Federated Learningcited by 383
  11. 2019The Non-IID Data Quagmire of Decentralized Machine Learningcited by 304
  12. 2018Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Datacited by 243
17 more
  1. 2021No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Datacited by 241
  2. 2020Federated Visual Classification with Real-World Data Distributioncited by 127
  3. 2021FedMix: Approximation of Mixup under Mean Augmented Federated Learningcited by 108
  4. 2020Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learningcited by 78
  5. 2018Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learningcited by 76
  6. 2018Lagrange Coded Computing: Optimal Design for Resiliency, Security and Privacycited by 66
  7. 2021Towards Fair Federated Learning with Zero-Shot Data Augmentationcited by 64
  8. 2020Scheduling for Cellular Federated Edge Learning with Importance and Channel Awarenesscited by 62
  9. 2020FastSecAgg: Scalable Secure Aggregation for Privacy-Preserving Federated Learningcited by 61
  10. 2019Hybrid-FL for Wireless Networks: Cooperative Learning Mechanism Using Non-IID Datacited by 38
  11. 2022Federated Learning with GAN-based Data Synthesis for Non-IID Clientscited by 25
  12. 2021FedDPGAN: Federated Differentially Private Generative Adversarial Networks Framework for the Detection of COVID-19 Pneumoniacited by 22
  13. 2020A Scalable Approach for Privacy-Preserving Collaborative Machine Learningcited by 21
  14. 2019CodedPrivateML: A Fast and Privacy-Preserving Framework for Distributed Machine Learningcited by 20
  15. 2021Federated Learning for COVID-19 Detection with Generative Adversarial Networks in Edge Cloud Computingcited by 18
  16. 2022SwiftAgg+: Achieving Asymptotically Optimal Communication Loads in Secure Aggregation for Federated Learningcited by 18
  17. 2022Stochastic Coded Federated Learning with Convergence and Privacy Guaranteescited by 10

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2022: 2; 2023: 8; 2024: 4; 2025: 2; 2026: 1.

17 papers.

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  1. 2022FedFA: Federated Learning with Feature Anchors to Align Features and Classifiers for Heterogeneous Datacited by 50
  2. 2023Privacy and Fairness in Federated Learning: on the Perspective of Trade-offcited by 26
  3. 2023Selective Knowledge Sharing for Privacy-Preserving Federated Distillation without A Good Teachercited by 25
  4. 2023Green Edge AI: A Contemporary Surveycited by 24
  5. 2023A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiencycited by 21
  6. 2023FedVS: Straggler-Resilient and Privacy-Preserving Vertical Federated Learning for Split Modelscited by 17
  7. 2024Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directionscited by 13
  8. 2023MimiC: Combating Client Dropouts in Federated Learning by Mimicking Central Updatescited by 10
  9. 2023Understanding and Improving Model Averaging in Federated Learning on Heterogeneous Datacited by 7
  10. 2024Achieving Linear Speedup in Asynchronous Federated Learning with Heterogeneous Clientscited by 7
  11. 2022Stochastic Coded Federated Learning: Theoretical Analysis and Incentive Mechanism Designcited by 6
  12. 2023Analysis and Optimization of Wireless Federated Learning with Data Heterogeneitycited by 3
5 more
  1. 2024DMM: Distributed Matrix Mechanism for Differentially-Private Federated Learning Based on Constant-Overhead Linear Secret Resharingcited by 3
  2. 2024Design and Optimization of Hierarchical Gradient Coding for Distributed Learning at Edge Devicescited by 0
  3. 2025OmniFC: Rethinking Federated Clustering via Lossless and Secure Distance Reconstructioncited by 0
  4. 2025Mitigating Persistent Client Dropout in Asynchronous Decentralized Federated Learningcited by 0
  5. 2026FedEHR-Agents: Federated Agentic Optimization for Automated EHR Modelingcited by 0