Federated Learning Labs
Tools for privacy preserving data science. Run machine learning or simple analytics on distributed data.
Our Mission
Our goal is to make machine learning possible on sensitive data without compromising privacy. We enable this process across multiple data providers, unleashing the aggregated power of distributed datasets. We strive for simplicity, allowing anyone to set up a project. Connecting data scientists with data providers is a matter of minutes.
Learn MoreServices
Nocode platform
Web application for running federated learning easily.
Integrations
Federated Learning as a Service. Integrate FELT into web applications with our SDK.
Consulting
Data science support, algorithms for specific use-cases.
Infrastructure
We provide compute environments for running CtD jobs
Our Roadmap
Nov 2021
Birth
Birth of FELT on Chainlink Hackathon
2022
Architectural Upgrade
We decided to create FELT on top of Ocean Protocol stack.
MVP
FELT v.0.1.0 built on top of Ocean Protocol stack
Ocean Shipyard
We joined Ocean Shipyard - early stage grant program
2023
New Features
Running 3rd party algorithms, automation with python flow, support new AI models.
Marketplace Integration
Easier access to FELT directly from Ocean Marketplace using FELT SDK
Generative AI
Ability to fine-tune LLM models on private data
Organization Feature
Dashboard for organizations to manage their projects and members
Our Team
Břetislav Hájek
Filip Masár
Peter Žáčik
Blog Posts
Fine-Tuning Large Language Models with FELT Labs — Full Guide
You can now fine-tune large language models (LLMs) on your private data using our algorithms without the need to set up your own architecture…
Guide to Federated Learning on Blockchain with Ocean Protocol
Today we will present the first version of the FELT Labs tool for federated learning on Ocean protocol…
Evaluating Federated Learning from FELT Labs on MNIST Dataset
FELT Labs is a tool for training machine learning models on multiple decentralized datasets. 'Does it actually work?' ...
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