mlsanityData to 14 Oct 2026

CodedPaddedFL and CodedSecAgg: Straggler Mitigation and Secure Aggregation in Federated Learning

arXiv 2112.08909 ↗First posted 16 Dec 2021cs.LG cs.CR cs.ITcited by 20

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Abstract (arXiv)

We present two novel federated learning (FL) schemes that mitigate the effect of straggling devices by introducing redundancy on the devices' data across the network. Compared to other schemes in the literature, which deal with stragglers or device dropouts by ignoring their contribution, the proposed schemes do not suffer from the client drift problem. The first scheme, CodedPaddedFL, mitigates the effect of stragglers while retaining the privacy level of conventional FL. It combines one-time padding for user data privacy with gradient codes to yield straggler resiliency. The second scheme, CodedSecAgg, provides straggler resiliency and robustness against model inversion attacks and is based on Shamir's secret sharing. We apply CodedPaddedFL and CodedSecAgg to a classification problem. For a scenario with 120 devices, CodedPaddedFL achieves a speed-up factor of 18 for an accuracy of 95% on the MNIST dataset compared to conventional FL. Furthermore, it yields similar performance in terms of latency compared to a recently proposed scheme by Prakash et al. without the shortcoming of additional leakage of private data. CodedSecAgg outperforms the state-of-the-art secure aggregation scheme LightSecAgg by a speed-up factor of 6.6-18.7 for the MNIST dataset for an accuracy of 95%.

Key facts. Cited by 20 papers in the map (data through 14 Oct 2026). Builds on 34 of its 57 references that are in the map. First posted on arXiv 16 Dec 2021.

Builds on

34 of 57 references.

References · 34

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22 more
  1. 2021Federated Learning with Buffered Asynchronous Aggregationcited by 190
  2. 2021Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradientscited by 123
  3. 2019SAFA: a Semi-Asynchronous Protocol for Fast Federated Learning with Low Overheadcited by 82
  4. 2020Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learningcited by 78
  5. 2015Speeding Up Distributed Machine Learning Using Codescited by 76
  6. 2018Lagrange Coded Computing: Optimal Design for Resiliency, Security and Privacycited by 66
  7. 2021LightSecAgg: a Lightweight and Versatile Design for Secure Aggregation in Federated Learningcited by 64
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  10. 2017Polynomial Codes: an Optimal Design for High-Dimensional Coded Matrix Multiplicationcited by 32
  11. 2020Coded Computing for Low-Latency Federated Learning over Wireless Edge Networkscited by 32
  12. 2018On the Optimal Recovery Threshold of Coded Matrix Multiplicationcited by 27
  13. 2017Straggler Mitigation in Distributed Optimization Through Data Encodingcited by 26
  14. 2020Secure Aggregation with Heterogeneous Quantization in Federated Learningcited by 21
  15. 2021Information Theoretic Secure Aggregation with User Dropoutscited by 19
  16. 2019Asynchronous Federated Learning with Differential Privacy for Edge Intelligencecited by 16
  17. 2017Coded Computation over Heterogeneous Clusterscited by 12
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 6; 2024: 6; 2025: 6; 2026: 1.

20 papers.

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  1. 2022SwiftAgg+: Achieving Asymptotically Optimal Communication Loads in Secure Aggregation for Federated Learningcited by 18
  2. 2023FedVS: Straggler-Resilient and Privacy-Preserving Vertical Federated Learning for Split Modelscited by 17
  3. 2023FLuID: Mitigating Stragglers in Federated Learning using Invariant Dropoutcited by 16
  4. 2023MimiC: Combating Client Dropouts in Federated Learning by Mimicking Central Updatescited by 10
  5. 2024A Comprehensive Survey of Federated Transfer Learning: Challenges, Methods and Applicationscited by 8
  6. 2023Private Aggregation in Hierarchical Wireless Federated Learning with Partial and Full Collusioncited by 5
  7. 2023ByzSecAgg: A Byzantine-Resistant Secure Aggregation Scheme for Federated Learning Based on Coded Computing and Vector Commitmentcited by 4
  8. 2024Stragglers-Aware Low-Latency Synchronous Federated Learning via Layer-Wise Model Updatescited by 4
  9. 2024Communication-Efficient Byzantine-Resilient Federated Zero-Order Optimizationcited by 4
  10. 2023FLIPS: Federated Learning using Intelligent Participant Selectioncited by 2
  11. 2026Secure Aggregation for Privacy-Preserving Federated Learning on Clinical EEG Datacited by 1
  12. 2024Approximate Gradient Coding for Privacy-Flexible Federated Learning with Non-IID Datacited by 0
8 more
  1. 2024Design and Optimization of Hierarchical Gradient Coding for Distributed Learning at Edge Devicescited by 0
  2. 2024Multi-Model based Federated Learning Against Model Poisoning Attack: A Deep Learning Based Model Selection for MEC Systemscited by 0
  3. 2025CO-DEFEND: Continuous Decentralized Federated Learning for Secure DoH-Based Threat Detectioncited by 0
  4. 2025Federated One-Shot Learning with Data Privacy and Objective-Hidingcited by 0
  5. 2025Private Aggregation for Byzantine-Resilient Heterogeneous Federated Learningcited by 0
  6. 2025Perfectly-Private Analog Secure Aggregation in Federated Learningcited by 0
  7. 2025Hetero-SplitEE: Split Learning of Neural Networks with Early Exits for Heterogeneous IoT Devicescited by 0
  8. 2025MURIM: Multidimensional Reputation-based Incentive Mechanism for Federated Learningcited by 0