# EDLaaS: Fully Homomorphic Encryption Over Neural Network Graphs for Vision and Private Strawberry Yield Forecasting

arXiv: https://arxiv.org/abs/2110.13638 · HTML: https://mlsanity.com/paper/2110.13638/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 53 references that are in the map.
- First posted on arXiv 26 Oct 2021.

## Builds on (most cited first, 8 of 8)

- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Gazelle: A Low Latency Framework for Secure Neural Network Inference](https://mlsanity.com/paper/1801.05507/) (2018)
- [Privacy-Preserving Machine Learning with Fully Homomorphic Encryption for Deep Neural Network](https://mlsanity.com/paper/2106.07229/) (2021)
- [Contrastive Domain Adaptation](https://mlsanity.com/paper/2103.15566/) (2021)
- [EVA: An Encrypted Vector Arithmetic Language and Compiler for Efficient Homomorphic Computation](https://mlsanity.com/paper/1912.11951/) (2019)
- [The Role of Cross-Silo Federated Learning in Facilitating Data Sharing in the Agri-Food Sector](https://mlsanity.com/paper/2104.07468/) (2021)
- [Fully Homomorphically Encrypted Deep Learning as a Service](https://mlsanity.com/paper/2107.12997/) (2021)
- [Nemesyst: A Hybrid Parallelism Deep Learning-Based Framework Applied for Internet of Things Enabled Food Retailing Refrigeration Systems](https://mlsanity.com/paper/1906.01600/) (2019)

## Built on by (most cited first, 1 of 1)

- [Exploiting Unstructured Sparsity in Fully Homomorphic Encrypted DNNs](https://mlsanity.com/paper/2503.09184/) (2025)

## Abstract (arXiv)

We present automatically parameterised Fully Homomorphic Encryption (FHE) for encrypted neural network inference and exemplify our inference over FHE compatible neural networks with our own open-source framework and reproducible examples. We use the 4th generation Cheon, Kim, Kim and Song (CKKS) FHE scheme over fixed points provided by the Microsoft Simple Encrypted Arithmetic Library (MS-SEAL). We significantly enhance the usability and applicability of FHE in deep learning contexts, with a focus on the constituent graphs, traversal, and optimisation. We find that FHE is not a panacea for all privacy preserving machine learning (PPML) problems, and that certain limitations still remain, such as model training. However we also find that in certain contexts FHE is well suited for computing completely private predictions with neural networks. The ability to privately compute sensitive problems more easily, while lowering the barriers to entry, can allow otherwise too-sensitive fields to begin advantaging themselves of performant third-party neural networks. Lastly we show how encrypted deep learning can be applied to a sensitive real world problem in agri-food, i.e. strawberry yield forecasting, demonstrating competitive performance. We argue that the adoption of encrypted deep learning methods at scale could allow for a greater adoption of deep learning methodologies where privacy concerns exists, hence having a large positive potential impact within the agri-food sector and its journey to net zero.