Pre-Alpha Stage
DeML OS
The infrastructure to execute, verify, settle, and constrain ML autonomously.
Research Scope
Defining the boundaries of decentralized machine learning systems
In Scope
- ML execution under non-centralized scheduling
- Trust and correctness of ML in untrusted environments
- Native pricing and settlement mechanisms that support long-term operation
Out of Scope
- ✗ Systems that only change payment methods without changing ML execution
- ✗ Pure CPU / GPU compute marketplaces
- ✗ AI products or agent applications
System Dimensions
Key evaluation criteria for decentralized ML systems
Execution
Inference, training, and task scheduling mechanisms
Verification
Trust models, correctness proofs, and dispute resolution
Incentive
Pricing models, incentive mechanisms, and sustainability
Governance
Upgradeability, decision-making, and tokenomics
Recent Research Notes
Latest developments in decentralized ML
Paper
VISTA: Decentralized Machine Learning in Adversary Dominated Environments
Hanzaleh Akbari Nodehi DeML
Paper
Can Blockchains Reliably Train Machine Learning Models?
Peihao Li PoW
Paper
AgentReputation: A Decentralized Agentic AI Reputation Framework
Mohd Sameen Chishti Reputation
Paper
TRUST: A Framework for Decentralized AI Service v.0.1
Yu-Chao Huang Decentralized AI
Paper
Cloudless-Training: A Framework to Improve Efficiency of Geo-Distributed ML Training
Wenting Tan Geo-Distributed ML
Join the Research Community
If you are interested in distributed systems, ML systems, verifiable computation, or crypto-economic mechanisms at the system layer, you are welcome to join the discussion.