mlsanityData to 7 Oct 2026

Secure Aggregation with Heterogeneous Quantization in Federated Learning

arXiv 2009.14388 ↗First posted 30 Sep 2020cs.IT cs.SY eess.SYcited by 21

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Abstract (arXiv)

Secure model aggregation across many users is a key component of federated learning systems. The state-of-the-art protocols for secure model aggregation, which are based on additive masking, require all users to quantize their model updates to the same level of quantization. This severely degrades their performance due to lack of adaptation to available bandwidth at different users. We propose three schemes that allow secure model aggregation while using heterogeneous quantization. This enables the users to adjust their quantization proportional to their available bandwidth, which can provide a substantially better trade-off between the accuracy of training and the communication time. The proposed schemes are based on a grouping strategy by partitioning the network into groups, and partitioning the local model updates of users into segments. Instead of applying aggregation protocol to the entire local model update vector, it is applied on segments with specific coordination between users. We theoretically evaluate the quantization error for our schemes, and also demonstrate how our schemes can be utilized to overcome Byzantine users.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Sep 2020.

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Citing papers per year: 2021: 3; 2022: 5; 2023: 5; 2024: 4; 2025: 3; 2026: 1.

21 papers.

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  1. 2023LOKI: Large-scale Data Reconstruction Attack against Federated Learning through Model Manipulationcited by 35
  2. 2021CodedPaddedFL and CodedSecAgg: Straggler Mitigation and Secure Aggregation in Federated Learningcited by 20
  3. 2021Information Theoretic Secure Aggregation with User Dropoutscited by 19
  4. 2022How Much Privacy Does Federated Learning with Secure Aggregation Guarantee?cited by 19
  5. 2023Mixed-Precision Quantization for Federated Learning on Resource-Constrained Heterogeneous Devicescited by 11
  6. 2021Secure Aggregation for Buffered Asynchronous Federated Learningcited by 10
  7. 2022Enhancing Heterogeneous Federated Learning with Knowledge Extraction and Multi-Model Fusioncited by 10
  8. 2023MimiC: Combating Client Dropouts in Federated Learning by Mimicking Central Updatescited by 10
  9. 2023Federated Analytics: A surveycited by 9
  10. 2023The Resource Problem of Using Linear Layer Leakage Attack in Federated Learningcited by 9
  11. 2025Fundamental Limits of Hierarchical Secure Aggregation with Cyclic User Associationcited by 7
  12. 2024Privacy-Preserving Aggregation for Decentralized Learning with Byzantine-Robustnesscited by 4
9 more
  1. 2025From Centralized to Decentralized Federated Learning: Theoretical Insights, Privacy Preservation, and Robustness Challengescited by 4
  2. 2024Linkage on Security, Privacy and Fairness in Federated Learning: New Balances and New Perspectivescited by 3
  3. 2024NeurIPS 2023 Competition: Privacy Preserving Federated Learning Document VQAcited by 2
  4. 2022FedNet2Net: Saving Communication and Computations in Federated Learning with Model Growingcited by 1
  5. 2022Communication-Efficient {Federated} Learning Using Censored Heavy Ball Descentcited by 1
  6. 2022Optimal Privacy Preserving for Federated Learning in Mobile Edge Computingcited by 1
  7. 2024Differentially Private Federated Learning without Noise Addition: When is it Possible?cited by 0
  8. 2025Progressive Size-Adaptive Federated Learning: A Comprehensive Framework for Heterogeneous Multi-Modal Data Systemscited by 0
  9. 2026Quantization in Federated Learning: Methods, Challenges and Future Directionscited by 0