# Efficient Differentially Private Secure Aggregation for Federated Learning via Hardness of Learning with Errors

arXiv: https://arxiv.org/abs/2112.06872 · HTML: https://mlsanity.com/paper/2112.06872/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 58 references that are in the map.
- First posted on arXiv 13 Dec 2021.

## Builds on (most cited first, 21 of 21)

- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Membership Inference Attacks against Machine Learning Models](https://mlsanity.com/paper/1610.05820/) (2016)
- [The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks](https://mlsanity.com/paper/1802.08232/) (2018)
- [Renyi Differential Privacy](https://mlsanity.com/paper/1702.07476/) (2017)
- [Federated Learning with Differential Privacy: Algorithms and Performance Analysis](https://mlsanity.com/paper/1911.00222/) (2019)
- [Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training](https://mlsanity.com/paper/1712.01887/) (2017)
- [Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning](https://mlsanity.com/paper/1702.07464/) (2017)
- [Data Poisoning Attacks Against Federated Learning Systems](https://mlsanity.com/paper/2007.08432/) (2020)
- [A Hybrid Approach to Privacy-Preserving Federated Learning](https://mlsanity.com/paper/1812.03224/) (2018)
- [Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity](https://mlsanity.com/paper/1811.12469/) (2018)
- [LDP-Fed: Federated Learning with Local Differential Privacy](https://mlsanity.com/paper/2006.03637/) (2020)
- [Protection Against Reconstruction and Its Applications in Private Federated Learning](https://mlsanity.com/paper/1812.00984/) (2018)
- [The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation](https://mlsanity.com/paper/2102.06387/) (2021)
- [Distributed Differential Privacy via Shuffling](https://mlsanity.com/paper/1808.01394/) (2018)
- [Deep Learning with Gaussian Differential Privacy](https://mlsanity.com/paper/1911.11607/) (2019)
- [Revisiting Membership Inference Under Realistic Assumptions](https://mlsanity.com/paper/2005.10881/) (2020)
- [Byzantine-Resilient Secure Federated Learning](https://mlsanity.com/paper/2007.11115/) (2020)
- [POSEIDON: Privacy-Preserving Federated Neural Network Learning](https://mlsanity.com/paper/2009.00349/) (2020)
- [A Better Bound Gives a Hundred Rounds: Enhanced Privacy Guarantees via f-Divergences](https://mlsanity.com/paper/2001.05990/) (2020)
- [Shrinkwrap: Differentially-Private Query Processing in Private Data Federations](https://mlsanity.com/paper/1810.01816/) (2018)

## Built on by (most cited first, 25 of 25)

- [DP-Forward: Fine-tuning and Inference on Language Models with Differential Privacy in Forward Pass](https://mlsanity.com/paper/2309.06746/) (2023)
- [Flamingo: Multi-Round Single-Server Secure Aggregation with Applications to Private Federated Learning](https://mlsanity.com/paper/2308.09883/) (2023)
- [A Survey for Federated Learning Evaluations: Goals and Measures](https://mlsanity.com/paper/2308.11841/) (2023)
- [Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey](https://mlsanity.com/paper/2505.01821/) (2025)
- [Spectral-DP: Differentially Private Deep Learning through Spectral Perturbation and Filtering](https://mlsanity.com/paper/2307.13231/) (2023)
- [Privacy-Preserving Federated Unlearning with Certified Client Removal](https://mlsanity.com/paper/2404.09724/) (2024)
- [Correlated Privacy Mechanisms for Differentially Private Distributed Mean Estimation](https://mlsanity.com/paper/2407.03289/) (2024)
- [Federated Learning: A Cutting-Edge Survey of the Latest Advancements and Applications](https://mlsanity.com/paper/2310.05269/) (2023)
- [A Survey on Cloud-Edge-Terminal Collaborative Intelligence in AIoT Networks](https://mlsanity.com/paper/2508.18803/) (2025)
- [The Power of Bias: Optimizing Client Selection in Federated Learning with Heterogeneous Differential Privacy](https://mlsanity.com/paper/2408.08642/) (2024)
- [Generalizing Differentially Private Decentralized Deep Learning with Multi-Agent Consensus](https://mlsanity.com/paper/2306.13892/) (2023)
- [On Using Secure Aggregation in Differentially Private Federated Learning with Multiple Local Steps](https://mlsanity.com/paper/2407.19286/) (2024)
- [When approximate design for fast homomorphic computation provides differential privacy guarantees](https://mlsanity.com/paper/2304.02959/) (2023)
- [Differentially Private Secure Multiplication: Hiding Information in the Rubble of Noise](https://mlsanity.com/paper/2309.16105/) (2023)
- [Fluent: Round-efficient Secure Aggregation for Private Federated Learning](https://mlsanity.com/paper/2403.06143/) (2024)
- [Privacy-Preserving, Dropout-Resilient Aggregation in Decentralized Learning](https://mlsanity.com/paper/2404.17984/) (2024)
- [A Selective Homomorphic Encryption Approach for Faster Privacy-Preserving Federated Learning](https://mlsanity.com/paper/2501.12911/) (2025)
- [Moss: Proxy Model-based Full-Weight Aggregation in Federated Learning with Heterogeneous Models](https://mlsanity.com/paper/2503.10218/) (2025)
- [ParaAegis: Parallel Protection for Flexible Privacy-preserved Federated Learning](https://mlsanity.com/paper/2509.13739/) (2025)
- [SoK: Enhancing Cryptographic Collaborative Learning with Differential Privacy](https://mlsanity.com/paper/2601.09460/) (2026)
- [Scalable and Private Federated Learning Using Distributed Differential Privacy and Secure Aggregation](https://mlsanity.com/paper/2604.07125/) (2026)
- [Secure and Privacy-Preserving Vertical Federated Learning](https://mlsanity.com/paper/2604.13474/) (2026)
- [Local Differential Privacy with Correlated Noise Achieves Central-DP Optimal Cost](https://mlsanity.com/paper/2605.30476/) (2026)
- [Your Privacy My Cloak: Backdoor Attacks on Differentially Private Federated Learning](https://mlsanity.com/paper/2606.17035/) (2026)
- [When Topology Betrays Privacy: Lattice-Based Reconstruction Attacks on Secure Aggregation in Decentralized Federated Learning](https://mlsanity.com/paper/2609.08476/) (2026)

## Abstract (arXiv)

Federated machine learning leverages edge computing to develop models from network user data, but privacy in federated learning remains a major challenge. Techniques using differential privacy have been proposed to address this, but bring their own challenges -- many require a trusted third party or else add too much noise to produce useful models. Recent advances in secure aggregation using multiparty computation eliminate the need for a third party, but are computationally expensive especially at scale. We present a new federated learning protocol that leverages a novel differentially private, malicious secure aggregation protocol based on techniques from Learning With Errors. Our protocol outperforms current state-of-the art techniques, and empirical results show that it scales to a large number of parties, with optimal accuracy for any differentially private federated learning scheme.