# Secure Aggregation with Heterogeneous Quantization in Federated Learning

arXiv: https://arxiv.org/abs/2009.14388 · HTML: https://mlsanity.com/paper/2009.14388/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Sep 2020.

## Built on by (most cited first, 21 of 21)

- [LOKI: Large-scale Data Reconstruction Attack against Federated Learning through Model Manipulation](https://mlsanity.com/paper/2303.12233/) (2023)
- [CodedPaddedFL and CodedSecAgg: Straggler Mitigation and Secure Aggregation in Federated Learning](https://mlsanity.com/paper/2112.08909/) (2021)
- [Information Theoretic Secure Aggregation with User Dropouts](https://mlsanity.com/paper/2101.07750/) (2021)
- [How Much Privacy Does Federated Learning with Secure Aggregation Guarantee?](https://mlsanity.com/paper/2208.02304/) (2022)
- [Mixed-Precision Quantization for Federated Learning on Resource-Constrained Heterogeneous Devices](https://mlsanity.com/paper/2311.18129/) (2023)
- [Secure Aggregation for Buffered Asynchronous Federated Learning](https://mlsanity.com/paper/2110.02177/) (2021)
- [Enhancing Heterogeneous Federated Learning with Knowledge Extraction and Multi-Model Fusion](https://mlsanity.com/paper/2208.07978/) (2022)
- [MimiC: Combating Client Dropouts in Federated Learning by Mimicking Central Updates](https://mlsanity.com/paper/2306.12212/) (2023)
- [Federated Analytics: A survey](https://mlsanity.com/paper/2302.01326/) (2023)
- [The Resource Problem of Using Linear Layer Leakage Attack in Federated Learning](https://mlsanity.com/paper/2303.14868/) (2023)
- [Fundamental Limits of Hierarchical Secure Aggregation with Cyclic User Association](https://mlsanity.com/paper/2503.04564/) (2025)
- [Privacy-Preserving Aggregation for Decentralized Learning with Byzantine-Robustness](https://mlsanity.com/paper/2404.17970/) (2024)
- [From Centralized to Decentralized Federated Learning: Theoretical Insights, Privacy Preservation, and Robustness Challenges](https://mlsanity.com/paper/2503.07505/) (2025)
- [Linkage on Security, Privacy and Fairness in Federated Learning: New Balances and New Perspectives](https://mlsanity.com/paper/2406.10884/) (2024)
- [NeurIPS 2023 Competition: Privacy Preserving Federated Learning Document VQA](https://mlsanity.com/paper/2411.03730/) (2024)
- [FedNet2Net: Saving Communication and Computations in Federated Learning with Model Growing](https://mlsanity.com/paper/2207.09568/) (2022)
- [Communication-Efficient {Federated} Learning Using Censored Heavy Ball Descent](https://mlsanity.com/paper/2209.11944/) (2022)
- [Optimal Privacy Preserving for Federated Learning in Mobile Edge Computing](https://mlsanity.com/paper/2211.07166/) (2022)
- [Differentially Private Federated Learning without Noise Addition: When is it Possible?](https://mlsanity.com/paper/2405.04551/) (2024)
- [Progressive Size-Adaptive Federated Learning: A Comprehensive Framework for Heterogeneous Multi-Modal Data Systems](https://mlsanity.com/paper/2506.20685/) (2025)
- [Quantization in Federated Learning: Methods, Challenges and Future Directions](https://mlsanity.com/paper/2606.26822/) (2026)

## 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.