Decentralised Federated Machine Learning on Polkadot
A protocol for training AI models across distributed nodes without centralising data. Governed by collective consensus.
Centralised AI Limits Access and Control
Today’s dominant AI models (OpenAI, Meta, Microsoft, etc.) require massive centralised compute and data. This limits access, raises safety and control concerns, and concentrates power in the hands of a few.
DecentralML aims to democratise AI through decentralised federated machine learning with transparent on-chain governance.
A Polkadot Protocol for DFML
DecentralML is a Polkadot protocol for decentralised federated machine learning (DFML) with collective safety consensus. Model weights, licensing, and jurisdiction rules are controlled transparently on-chain.
Privacy-preserving
Node or on-device training — data never leaves the source.
BYO Token Staking
Flexible economy using Substrate pallets (Balances, Grandpa, Ink!).
Collective Consensus
Model weights, licensing, and safety controls on-chain.
Built on Substrate
DecentralML leverages Substrate’s modular pallet architecture for seamless integration with the Polkadot ecosystem.
What You Can Do
Decentralised Federated ML
Train across nodes without centralising data.
Collective Governance
On-chain controls for model weights, licensing, safety, jurisdiction.
Node or On-Device Training
Privacy by design — data stays local.
Collaborative AI Training
Share training between organisations and nodes.
Polkadot-JS Apps
Connect via ws://localhost:9944.
Where It Fits
Privacy-preserving ML for healthcare, finance, or regulated sectors. Collaborative model training across organisations. Transparent, on-chain governance of AI models. Reducing centralisation and improving power and compute efficiency.
Run the Node
Clone the repo and follow the GitHub README for setup.
Use the Python client and Docker instructions in the repo. Connect via Polkadot-JS Apps.
Apache 2.0
DecentralML is fully open source. Contributions, forks, and ecosystem integration are encouraged.