The problem n0tte solves

The current machine learning (ML) landscape is controlled by a few major players who keep their high-quality trained models proprietary to maintain their dominance in the industry. This creates a significant obstacle for individuals, such as researchers, students, and small institutions, who want to leverage these high-quality models to analyze their own data.

n0tte addresses this issue by facilitating ML inference through Fully Homomorphic Encryption (FHE) within Ethereum Smart Contracts. This approach allows model owners to set the encrypted weights and biases of a pre-trained general-purpose 1-layer MLP model on a smart contract. Using the provided initiator set of Python scripts, model owners can train and quantize ML models and data off-chain. Consequently, any user can perform inference on their encrypted data directly on-chain, ensuring the privacy and security of user data.

Challenges we ran into

We encountered several challenges due to the novelty of the library & strictness of the EVM.

We are limited to shallow 1L MLP models on-chain. For example, we can use the model to perform regression and/or classification on a limited set of features with a drop in performances, due to the post-training quantization process. We could have represented MLP with multiple layers (or even deeper) but it would have been computationally burdensome.

Transaction space is limited resulting in a multi-txs fashion for setting the quantized & FHE encrypted weights, biases and data on the smart contract.

FHE is still cutting-edge research, experimental and slow compared to the state-of-the-art solutions. Thus, doing this on-chain, adds another layer of complexity (for now) due to limited nodes / validators computational resources & constraints of the EVM architecture.

The FHENIX FHE.sol represents unsigned integers from 8 to 32 bits (exclusively) - making impossible to represent negatives numbers significatively on-chain (we don't have space for fixed/float point math) - resulting in a huge problem since you cannot represent the entire range of fully-precision weights. We found out that calling 'FHE.mul()' and / or 'FHE.add()' breaks the smart contract state. Therefore, we opt in favor of the binary operators '*' and '+' (working on encrypted data).