# An End-to-End Encrypted Neural Network for Gradient Updates Transmission in Federated Learning

arXiv: https://arxiv.org/abs/1908.08340 · HTML: https://mlsanity.com/paper/1908.08340/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Aug 2019.

## Built on by (most cited first, 9 of 9)

- [Federated Learning on Non-IID Data: A Survey](https://mlsanity.com/paper/2106.06843/) (2021)
- [A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacy](https://mlsanity.com/paper/2302.10637/) (2023)
- [Federated Learning for Intrusion Detection System: Concepts, Challenges and Future Directions](https://mlsanity.com/paper/2106.09527/) (2021)
- [Pervasive AI for IoT applications: A Survey on Resource-efficient Distributed Artificial Intelligence](https://mlsanity.com/paper/2105.01798/) (2021)
- [PIVODL: Privacy-preserving vertical federated learning over distributed labels](https://mlsanity.com/paper/2108.11444/) (2021)
- [CG-FedLLM: How to Compress Gradients in Federated Fune-tuning for Large Language Models](https://mlsanity.com/paper/2405.13746/) (2024)
- [Auction Based Clustered Federated Learning in Mobile Edge Computing System](https://mlsanity.com/paper/2103.07150/) (2021)
- [A Novel Federated Learning-Based IDS for Enhancing UAVs Privacy and Security](https://mlsanity.com/paper/2312.04135/) (2023)
- [Encoded Gradients Aggregation against Gradient Leakage in Federated Learning](https://mlsanity.com/paper/2205.13216/) (2022)

## Abstract (arXiv)

Federated learning is a distributed learning method to train a shared model by aggregating the locally-computed gradient updates. In federated learning, bandwidth and privacy are two main concerns of gradient updates transmission. This paper proposes an end-to-end encrypted neural network for gradient updates transmission. This network first encodes the input gradient updates to a lower-dimension space in each client, which significantly mitigates the pressure of data communication in federated learning. The encoded gradient updates are directly recovered as a whole, i.e. the aggregated gradient updates of the trained model, in the decoding layers of the network on the server. In this way, gradient updates encrypted in each client are not only prevented from interception during communication, but also unknown to the server. Based on the encrypted neural network, a novel federated learning framework is designed in real applications. Experimental results show that the proposed network can effectively achieve two goals, privacy protection and data compression, under a little sacrifice of the model accuracy in federated learning.