mlsanityData to 7 Oct 2026

FLASHE: Additively Symmetric Homomorphic Encryption for Cross-Silo Federated Learning

arXiv 2109.00675 ↗First posted 2 Sep 2021cs.CR cs.DC cs.LGcited by 15

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Abstract (arXiv)

Homomorphic encryption (HE) is a promising privacy-preserving technique for cross-silo federated learning (FL), where organizations perform collaborative model training on decentralized data. Despite the strong privacy guarantee, general HE schemes result in significant computation and communication overhead. Prior works employ batch encryption to address this problem, but it is still suboptimal in mitigating communication overhead and is incompatible with sparsification techniques. In this paper, we propose FLASHE, an HE scheme tailored for cross-silo FL. To capture the minimum requirements of security and functionality, FLASHE drops the asymmetric-key design and only involves modular addition operations with random numbers. Depending on whether to accommodate sparsification techniques, FLASHE is optimized in computation efficiency with different approaches. We have implemented FLASHE as a pluggable module atop FATE, an industrial platform for cross-silo FL. Compared to plaintext training, FLASHE slightly increases the training time by ≤6%, with no communication overhead.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 23 of its 63 references that are in the map. First posted on arXiv 2 Sep 2021.

Builds on

23 of 63 references.

References · 23

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11 more
  1. 2020Personalized Cross-Silo Federated Learning on Non-IID Datacited by 282
  2. 2017Sparse Communication for Distributed Gradient Descentcited by 243
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  8. 2021TenSEAL: A Library for Encrypted Tensor Operations Using Homomorphic Encryptioncited by 51
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 0; 2023: 5; 2024: 3; 2025: 5; 2026: 1.

15 papers.

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  1. 2023FedML-HE: An Efficient Homomorphic-Encryption-Based Privacy-Preserving Federated Learning Systemcited by 38
  2. 2023A Survey on Vertical Federated Learning: From a Layered Perspectivecited by 30
  3. 2023Privacy and Fairness in Federated Learning: on the Perspective of Trade-offcited by 26
  4. 2024Threats and Defenses in Federated Learning Life Cycle: A Comprehensive Survey and Challengescited by 14
  5. 2021Towards Efficient Synchronous Federated Training: A Survey on System Optimization Strategiescited by 9
  6. 2025Research in Collaborative Learning Does Not Serve Cross-Silo Federated Learning in Practicecited by 3
  7. 2023Federated Learning is Better with Non-Homomorphic Encryptioncited by 2
  8. 2023Cross-Silo Federated Learning Across Divergent Domains with Iterative Parameter Alignmentcited by 1
  9. 2024PackVFL: Efficient HE Packing for Vertical Federated Learningcited by 1
  10. 2025DictPFL: Efficient and Private Federated Learning on Encrypted Gradientscited by 1
  11. 2025Hi-SAFE: Hierarchical Secure Aggregation for Lightweight Federated Learningcited by 1
  12. 2024Chu-ko-nu: A Reliable, Efficient, and Anonymously Authentication-Enabled Realization for Multi-Round Secure Aggregation in Federated Learningcited by 0
3 more
  1. 2025SenseCrypt: Sensitivity-guided Selective Homomorphic Encryption for Joint Federated Learning in Cross-Device Scenarioscited by 0
  2. 2025CryptoTensors: A Light-Weight Large Language Model File Format for Highly-Secure Model Distributioncited by 0
  3. 2026Privacy-Preserving Federated Learning via Differential Privacy and Homomorphic Encryption for Cardiovascular Disease Risk Modelingcited by 0