SoraChain AI builds infrastructure-agnostic protocol rails for trustless federated learning, enabling verifiable model updates across private datasets and global training networks. The platform integrates with industry frameworks like OpenFL, PyTorch, and NVFLARE, and leverages TEEs and IPFS to ensure secure, decentralized model coordination.
Funding
Funding not disclosed
Founders
Product
Problem
Federated learning relies on centralized coordinators or opaque trust assumptions, making it difficult for independent parties to verify that model updates are legitimate and that private datasets remain protected. This limits the scalability of collaborative AI training across untrusted networks and enterprises.
Solution
SoraChain AI provides protocol-level infrastructure for running federated learning in a trustless manner across heterogeneous environments. The platform is infrastructure-agnostic, integrating with leading federated learning frameworks such as OpenFL, PyTorch, and NVFLARE, while adding a verification and coordination layer that ensures model updates are cryptographically auditable. It leverages trusted execution environments (TEEs) for secure computation and IPFS for decentralized model and update storage, creating a global training network where participants can contribute data without surrendering privacy or control.
Target Audience
Primary users are AI research consortia, enterprises running multi-party collaborative training, and Web3 organizations seeking verifiable and privacy-preserving federated learning infrastructure.
Features
- Trustless verification layer for model updates using cryptographic attestation and TEE-based secure enclaves
- Infrastructure-agnostic protocol rails compatible with OpenFL, PyTorch, NVFLARE, and OPA policy engines
- Decentralized storage of model artifacts and update records via IPFS
- Support for private datasets with privacy-preserving computation and policy-based access control
- Designed for global, multi-party training networks with verifiable contribution tracking