The answer:
Your model or your data stays with its owner.
The computation travels to it, not the data to the model
HFL · Federated Fine-Tuning
Problem
Your model is deployed inside the client's perimeter. Drift sets in, accuracy drops, yet your SLA says you must hold it.
You need to retrain on the client's new data, but that data can't leave their perimeter.
Solution
Fine-tune the model right inside the client's perimeter. The model travels to the data.
Accuracy holds, data never moves, and the model stays yours.
You own the model
Best for
Vertical Federated Learning
Problem
Banks want to enrich their models with your data - scoring, fraud, risk.
The demand is there, but you can't capture it: selling raw data isn't an option, and a dataset you sell stops being your asset.
Solution
Your data becomes a connectable service instead of a dataset for sale.
A client connects to your node and trains a shared model. Neither side sees the other's raw data. The data stays with you; you earn on every inference.
You own the data
Best for
No Python, cryptography or FL expertise required
The vendor controls training; the client only runs the model inside their own perimeter
Vendor side
Full control: training, Start/Stop, GPU
Client side
Read-only, no controls
Participants connect their nodes and train a shared model without revealing raw data.
Workspace
invite nodes and manage project participants.
Secure transmission of ML model parameters
No data storage
Our product handles it all — you don’t need to be a Python guru, FL expert, cryptographer, data scientist, or sysadmin
Obtaining the network's response in a secure manner
Secure Inference
Meet internal security and compliance standards to prevent project interruptions
Benefit
Enhance ML model quality
without raw data transmission
Benefit
Monetize knowledge safely,
not datasets
Benefit
Stand out by ensuring
data confidentiality
Benefit
Comply with data
security regulations
Federated Learning
Building a Collective Brain from Scattered Thoughts
FL allows multiple parties to contribute information to train a machine learning model, all while keeping their original data private.
It's like a collaborative brainstorming session where everyone contributes ideas without revealing their thought process.
Homomorphic Encryption
Private Computation on Model Parameters — No Decryption Needed
Think of it as solving a puzzle without ever taking it out of the box. With HE, model parameters stay encrypted even during training and inference.
No raw data is touched — and yet, everything works. This means ultra-secure collaboration with zero compromise on model quality.
Differential Privacy
Noise Where It Matters
By adding calibrated noise to model parameters, it prevents leakage of individual information.
The result? Robust privacy with models that stay sharp and effective.
