# Distributed Additive Encryption and Quantization for Privacy Preserving Federated Deep Learning

arXiv: https://arxiv.org/abs/2011.12623 · HTML: https://mlsanity.com/paper/2011.12623/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2020.

## Built on by (most cited first, 13 of 13)

- [Federated Learning on Non-IID Data: A Survey](https://mlsanity.com/paper/2106.06843/) (2021)
- [FedLess: Secure and Scalable Federated Learning Using Serverless Computing](https://mlsanity.com/paper/2111.03396/) (2021)
- [PIVODL: Privacy-preserving vertical federated learning over distributed labels](https://mlsanity.com/paper/2108.11444/) (2021)
- [Study of the performance and scalability of federated learning for medical imaging with intermittent clients](https://mlsanity.com/paper/2207.08581/) (2022)
- [A Federated Data-Driven Evolutionary Algorithm](https://mlsanity.com/paper/2102.08288/) (2021)
- [Privacy-Preserving in Blockchain-based Federated Learning Systems](https://mlsanity.com/paper/2401.03552/) (2024)
- [Vertical federated learning based on DFP and BFGS](https://mlsanity.com/paper/2101.09428/) (2021)
- [SPRITE: A Scalable Privacy-Preserving and Verifiable Collaborative Learning for Industrial IoT](https://mlsanity.com/paper/2203.11914/) (2022)
- [Adjustable Privacy using Autoencoder-based Learning Structure](https://mlsanity.com/paper/2304.03538/) (2023)
- [Bits for Privacy: Evaluating Post-Training Quantization via Membership Inference](https://mlsanity.com/paper/2512.15335/) (2025)
- [Privacy-Preserving Inference for Quantized BERT Models](https://mlsanity.com/paper/2508.01636/) (2025)
- [Upholding Robustness in Federated Learning: Trends, Emerging Strategies, and Research Opportunities](https://mlsanity.com/paper/2609.28722/) (2026)
- [PoCoFL: POlicy-COmpliant Federated Learning](https://mlsanity.com/paper/2610.03650/) (2026)

## Abstract (arXiv)

Homomorphic encryption is a very useful gradient protection technique used in privacy preserving federated learning. However, existing encrypted federated learning systems need a trusted third party to generate and distribute key pairs to connected participants, making them unsuited for federated learning and vulnerable to security risks. Moreover, encrypting all model parameters is computationally intensive, especially for large machine learning models such as deep neural networks. In order to mitigate these issues, we develop a practical, computationally efficient encryption based protocol for federated deep learning, where the key pairs are collaboratively generated without the help of a third party. By quantization of the model parameters on the clients and an approximated aggregation on the server, the proposed method avoids encryption and decryption of the entire model. In addition, a threshold based secret sharing technique is designed so that no one can hold the global private key for decryption, while aggregated ciphertexts can be successfully decrypted by a threshold number of clients even if some clients are offline. Our experimental results confirm that the proposed method significantly reduces the communication costs and computational complexity compared to existing encrypted federated learning without compromising the performance and security.