# Privacy-preserving Federated Learning based on Multi-key Homomorphic Encryption

arXiv: https://arxiv.org/abs/2104.06824 · HTML: https://mlsanity.com/paper/2104.06824/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Apr 2021.

## Built on by (most cited first, 30 of 48)

- [Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challenges](https://mlsanity.com/paper/2201.08135/) (2022)
- [Privacy-Preserving Machine Learning: Methods, Challenges and Directions](https://mlsanity.com/paper/2108.04417/) (2021)
- [FedML-HE: An Efficient Homomorphic-Encryption-Based Privacy-Preserving Federated Learning System](https://mlsanity.com/paper/2303.10837/) (2023)
- [Vision-Language Models for Edge Networks: A Comprehensive Survey](https://mlsanity.com/paper/2502.07855/) (2025)
- [DFRD: Data-Free Robustness Distillation for Heterogeneous Federated Learning](https://mlsanity.com/paper/2309.13546/) (2023)
- [Secure Neuroimaging Analysis using Federated Learning with Homomorphic Encryption](https://mlsanity.com/paper/2108.03437/) (2021)
- [Watermarking in Secure Federated Learning: A Verification Framework Based on Client-Side Backdooring](https://mlsanity.com/paper/2211.07138/) (2022)
- [Trustworthy Federated Learning: Privacy, Security, and Beyond](https://mlsanity.com/paper/2411.01583/) (2024)
- [Vertical Federated Learning: Taxonomies, Threats, and Prospects](https://mlsanity.com/paper/2302.01550/) (2023)
- [When Federated Learning meets Watermarking: A Comprehensive Overview of Techniques for Intellectual Property Protection](https://mlsanity.com/paper/2308.03573/) (2023)
- [Secure and Efficient Federated Learning in LEO Constellations using Decentralized Key Generation and On-Orbit Model Aggregation](https://mlsanity.com/paper/2309.01828/) (2023)
- [FheFL: Fully Homomorphic Encryption Friendly Privacy-Preserving Federated Learning with Byzantine Users](https://mlsanity.com/paper/2306.05112/) (2023)
- [Review of Mathematical Optimization in Federated Learning](https://mlsanity.com/paper/2412.01630/) (2024)
- [Effect of Homomorphic Encryption on the Performance of Training Federated Learning Generative Adversarial Networks](https://mlsanity.com/paper/2207.00263/) (2022)
- [Towards Fleet-wide Sharing of Wind Turbine Condition Information through Privacy-preserving Federated Learning](https://mlsanity.com/paper/2212.03529/) (2022)
- [Mitigating Cross-client GANs-based Attack in Federated Learning](https://mlsanity.com/paper/2307.13314/) (2023)
- [Privacy-Preserving Federated Learning with Consistency via Knowledge Distillation Using Conditional Generator](https://mlsanity.com/paper/2409.06955/) (2024)
- [A Numerical Gradient Inversion Attack in Variational Quantum Neural-Networks](https://mlsanity.com/paper/2504.12806/) (2025)
- [Privacy-Preserving Federated Learning via System Immersion and Random Matrix Encryption](https://mlsanity.com/paper/2204.02497/) (2022)
- [Federated Learning over Harmonized Data Silos](https://mlsanity.com/paper/2305.08985/) (2023)
- [Fed-Safe: Securing Federated Learning in Healthcare Against Adversarial Attacks](https://mlsanity.com/paper/2310.08681/) (2023)
- [Marking the Pace: A Blockchain-Enhanced Privacy-Traceable Strategy for Federated Recommender Systems](https://mlsanity.com/paper/2406.04702/) (2024)
- [Privacy-preserving gradient-based fair federated learning](https://mlsanity.com/paper/2407.13881/) (2024)
- [On Homomorphic Encryption Based Strategies for Class Imbalance in Federated Learning](https://mlsanity.com/paper/2410.21192/) (2024)
- [PPFPL: Cross-silo Privacy-preserving Federated Prototype Learning Against Data Poisoning Attacks](https://mlsanity.com/paper/2504.03173/) (2025)
- [Hi-SAFE: Hierarchical Secure Aggregation for Lightweight Federated Learning](https://mlsanity.com/paper/2511.18887/) (2025)
- [Decentralized Trust for Space AI: Blockchain-Based Federated Learning Across Multi-Vendor LEO Satellite Networks](https://mlsanity.com/paper/2512.08882/) (2025)
- [CQSA: Byzantine-robust Clustered Quantum Secure Aggregation in Federated Learning](https://mlsanity.com/paper/2602.22269/) (2026)
- [Secure & Private Federated Neuroimaging](https://mlsanity.com/paper/2205.05249/) (2022)
- [SABLE: Secure And Byzantine robust LEarning](https://mlsanity.com/paper/2309.05395/) (2023)

## Abstract (arXiv)

With the advance of machine learning and the internet of things (IoT), security and privacy have become key concerns in mobile services and networks. Transferring data to a central unit violates privacy as well as protection of sensitive data while increasing bandwidth demands.Federated learning mitigates this need to transfer local data by sharing model updates only. However, data leakage still remains an issue. In this paper, we propose xMK-CKKS, a multi-key homomorphic encryption protocol to design a novel privacy-preserving federated learning scheme. In this scheme, model updates are encrypted via an aggregated public key before sharing with a server for aggregation. For decryption, collaboration between all participating devices is required. This scheme prevents privacy leakage from publicly shared information in federated learning, and is robust to collusion between k<N-1 participating devices and the server. Our experimental evaluation demonstrates that the scheme preserves model accuracy against traditional federated learning as well as secure federated learning with homomorphic encryption (MK-CKKS, Paillier) and reduces computational cost compared to Paillier based federated learning. The average energy consumption is 2.4 Watts, so that it is suited to IoT scenarios.