# NOVA for Decentralized AI: Persistent Agent State for Multi-Chain Agents NOVA persists encrypted agent states/models between TEE sessions; enables multi-chain autonomy (e.g., cross-NEAR/Solana inference) with attestation chaining. ## Use Case In AI training (e.g., Phala's success story: ()) ## How NOVA Complements Private ML SDK Phala focuses on runtime privacy ("no storage, no logs"); NOVA adds decentralized persistence: encrypt/upload via composite\_upload, retrieve into TEE, output back. Focus: Data pipeline security without reinventing Phala's compute. NOVA handles pre-enclave sourcing (group auth) and post-enclave auditing (on-chain hashes/CIDs), extending Phala's quotes for full-lifecycle verifiability. ## Running the Demos * Set .env (NEAR\_PRIVATE\_KEY, etc.). * Rust: `cargo run --bin tee_federated_learning`. * JS: `ts-node demos/tee_federated_learning.ts`. Expected output: ``` Primary Account ID (Hospital A): Secondary Account ID (Hospital B): Hospital A uploaded to NOVA: CID QmW9oCqbrdMF8cKuYd14cwT3SszMWXRnaJ4aTZXKG4b1QA Waiting for IPFS pin to propagate... Hospital B output stored: CID QmSxuYEMhJpCm2wxC9zRQrbvaJN2irGtNdENCxRzs6J39C Waiting for IPFS pin to propagate... Hospital A final output stored: CID QmP5TiZYHByf1DDtXxVe5KjHj8r5Sp2E2MzLCMAxfH3Job ``` --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://civictech-ou.gitbook.io/nova-docs/use-cases/multi-agent-learning.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.