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# System prepended metadata

title: General Grant Proposal

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# FHE State-of-Knowledge 

* **Project:** FHE state-of-knowledge 

## Project Overview :page_facing_up: 

### Overview

In this grant, I will be procuring a knowledge base for current CKKS, TFHE, and hybrid CKKS-TFHE fully-homomorphic encryption (FHE) schemes used in machine learning models. The end result is to survey the current implementations of fully homomorphic encryption schemes and identify the key insights/tricks used in these machine learning implementations.

The common metrics that will be used to evaluate each FHE ML package is the effectiveness at solving various canonical machine learning problems (e.g. ImageNet, BERT, CIFAR, etc.) with the relevant machine learning architectures (CNN, Transformers, etc.). One paradigm of machine learning libraries of interest in particular are those that compile into the ONNX type (e.g. [HE-MAN](https://arxiv.org/abs/2302.08260)). Then, I will evaluate the models at  limit, benchmarking performance for the largest possible problem sizes. 

### Short Rationale
The utility of this survey will be in developing and informing the design choices of the Gauss Multi-Level Intermediate Representation (MLIR) FHE compiler. Specifically, the techniques gleaned from this survey can be used in optimizing operations, etc. 


### Project Details 
The expected final product is a HackMD that contains the knowledge base. Each entry in the document will contain benchmarking for that specific ML FHE library and a description of the various optimization tricks implemented. The benchmarking will be consolidated into a single repository. 

## Team :busts_in_silhouette:

### Team members
* Name: Bhargav Annem
* Email: bhargav.annem@gmail.com 
* Telegram: @OxWOLAND 
* Discord:  v0land
* Notion: bhargav.annem@gmail.com

### Team Website	
* Website: https://www.bhargav.nl/

### Team's experience
* Contributed to UniRep (a Zero-Knowledge Reputation/User Data Management Library).
* Built a Zero-Knowledge Virtual Machine using Chiquito (a Halo2 DSL).
* Developed a parallel fast number-theoretic transform library in Rust for polynomial operations

### Team Code Repos
* https://github.com/0xWOLAND/chiquito-vm
* https://github.com/0xWOLAND/fast-ntt


## Development Roadmap :nut_and_bolt: 

### Overview
* **Total Estimated Duration:** 8.5 weeks
* **Full-time equivalent (FTE):** 5.625 FTE
* **Total Costs:** $9,000

### Milestone 1 Example Application V1
* **Estimated Duration:** 8.5 weeks
* **FTE:**  5.625
* **Costs:** $9,000
* **Estimated delivery date**: April 13th, 2024

#### Deliverables and Specifications

##### 0b. Documentation

We will have a HackMD that surveys all of the various FHE schemes and our findings/insights.

##### 0c. Testing Guide

The FHE schemes will be benchmarked in a GitHub repository with tests for each linked FHE ML library and instructions on how to run the benchmarking.

##### 1. Application
The research findings in the documentation/knowledge base will be supported by the results generated by the benchmarking.

## Additional Information :heavy_plus_sign: 
The final survey will be published as a blog post for other FHE researchers to use and learn from. 