# Federated Learning with Autotuned Communication-Efficient Secure Aggregation

arXiv: https://arxiv.org/abs/1912.00131 · HTML: https://mlsanity.com/paper/1912.00131/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2019.

## Built on by (most cited first, 23 of 23)

- [The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation](https://mlsanity.com/paper/2102.06387/) (2021)
- [Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challenges](https://mlsanity.com/paper/2201.08135/) (2022)
- [LightSecAgg: a Lightweight and Versatile Design for Secure Aggregation in Federated Learning](https://mlsanity.com/paper/2109.14236/) (2021)
- [Federated Learning with Sparsified Model Perturbation: Improving Accuracy under Client-Level Differential Privacy](https://mlsanity.com/paper/2202.07178/) (2022)
- [Resource-Efficient Federated Learning](https://mlsanity.com/paper/2111.01108/) (2021)
- [Privacy and Fairness in Federated Learning: on the Perspective of Trade-off](https://mlsanity.com/paper/2306.14123/) (2023)
- [A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiency](https://mlsanity.com/paper/2307.10655/) (2023)
- [Information Theoretic Secure Aggregation with User Dropouts](https://mlsanity.com/paper/2101.07750/) (2021)
- [HCFL: A High Compression Approach for Communication-Efficient Federated Learning in Very Large Scale IoT Networks](https://mlsanity.com/paper/2204.06760/) (2022)
- [The Future of Consumer Edge-AI Computing](https://mlsanity.com/paper/2210.10514/) (2022)
- [Private Multi-Task Learning: Formulation and Applications to Federated Learning](https://mlsanity.com/paper/2108.12978/) (2021)
- [Secure Aggregation for Buffered Asynchronous Federated Learning](https://mlsanity.com/paper/2110.02177/) (2021)
- [Sustainable Federated Learning](https://mlsanity.com/paper/2102.11274/) (2021)
- [Interaction-Aware Gaussian Weighting for Clustered Federated Learning](https://mlsanity.com/paper/2502.03340/) (2025)
- [Federated Learning Algorithms for Generalized Mixed-effects Model (GLMM) on Horizontally Partitioned Data from Distributed Sources](https://mlsanity.com/paper/2109.14046/) (2021)
- [Reconciling Security and Communication Efficiency in Federated Learning](https://mlsanity.com/paper/2207.12779/) (2022)
- [Personalized Federated Domain Adaptation for Item-to-Item Recommendation](https://mlsanity.com/paper/2306.03191/) (2023)
- [Towards Adapting Federated & Quantum Machine Learning for Network Intrusion Detection: A Survey](https://mlsanity.com/paper/2509.21389/) (2025)
- [Optimal Client Sampling in Federated Learning with Client-Level Heterogeneous Differential Privacy](https://mlsanity.com/paper/2505.13655/) (2025)
- [Improving the Algorithm of Deep Learning with Differential Privacy](https://mlsanity.com/paper/2107.05457/) (2021)
- [DeFTA: A Plug-and-Play Decentralized Replacement for FedAvg](https://mlsanity.com/paper/2204.02632/) (2022)
- [Tackling Computational Heterogeneity in FL: A Few Theoretical Insights](https://mlsanity.com/paper/2307.06283/) (2023)
- [The Capacity of Information-Theoretic Secure Aggregation in Federated Learning](https://mlsanity.com/paper/2606.07277/) (2026)

## Abstract (arXiv)

Federated Learning enables mobile devices to collaboratively learn a shared inference model while keeping all the training data on a user's device, decoupling the ability to do machine learning from the need to store the data in the cloud. Existing work on federated learning with limited communication demonstrates how random rotation can enable users' model updates to be quantized much more efficiently, reducing the communication cost between users and the server. Meanwhile, secure aggregation enables the server to learn an aggregate of at least a threshold number of device's model contributions without observing any individual device's contribution in unaggregated form. In this paper, we highlight some of the challenges of setting the parameters for secure aggregation to achieve communication efficiency, especially in the context of the aggressively quantized inputs enabled by random rotation. We then develop a recipe for auto-tuning communication-efficient secure aggregation, based on specific properties of random rotation and secure aggregation -- namely, the predictable distribution of vector entries post-rotation and the modular wrapping inherent in secure aggregation. We present both theoretical results and initial experiments.