AttestAI
Decentralized AI framework with EAS verification to ensure data integrity, detect model poisoning, and securely store trained models on Walrus for trusted, tamper-proof machine learning solutions.
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About
The problem it solves **AttestAI** addresses critical challenges in decentralized AI and federated learning by ensuring data integrity, contributor verification, and secure operations. Here’s how it helps: 1. **Data Integrity Verification** - **Why it matters:** Federated learning relies on contributions from distributed nodes, which can introduce risks of malicious or low-quality data. - **How AttestAI helps:** By leveraging onchain attestations through EAS and Base, it verifies the authenticity of contributor nodes, their data, and infrastructure. This ensures that only reliable inputs are used for training, protecting the model from being poisoned or compromised. 2. **Secure and Transparent Operations** - **Why it matters:** Decentralized systems require trust among participants, which can be challenging without robust verification. - **How AttestAI helps:** AttestAI creates tamper-proof, auditable records of node contributions and attestations. This builds trust while simplifying participant tracking and system management. 3. **Effortless Wallet Integration** - **Why it matters:** Users need a seamless way to interact with decentralized systems. - **How AttestAI helps:** Using Base’s onchain kits, AttestAI integrates wallet functionality, allowing contributors to easily connect, manage their attestations, and track their role in the system securely and efficiently. 4. **Reusable Tools for Developers** - **Why it matters:** Building decentralized apps is complex, and tools that save time and effort can accelerate innovation. - **How AttestAI helps:** It offers modular, reusable components for managing attestations and onchain identity, making it easier for other developers to create secure, decentralized apps on Base. In summary, **AttestAI** makes federated learning and decentralized AI systems safer, simpler, and more transparent by solving key challenges related to trust, security, and usability in Web3 applications. Additional Features NA - Started right from ideation to build, during the Hackathon
How it's made
Challenges we ran into **AttestAI** addresses critical challenges in decentralized AI and federated learning by ensuring data integrity, contributor verification, and secure operations. Here’s how it helps: 1. **Data Integrity Verification** - **Why it matters:** Federated learning relies on contributions from distributed nodes, which can introduce risks of malicious or low-quality data. - **How AttestAI helps:** By leveraging onchain attestations through EAS and Base, it verifies the authenticity of contributor nodes, their data, and infrastructure. This ensures that only reliable inputs are used for training, protecting the model from being poisoned or compromised. 2. **Secure and Transparent Operations** - **Why it matters:** Decentralized systems require trust among participants, which can be challenging without robust verification. - **How AttestAI helps:** AttestAI creates tamper-proof, auditable records of node contributions and attestations. This builds trust while simplifying participant tracking and system management. 3. **Effortless Wallet Integration** - **Why it matters:** Users need a seamless way to interact with decentralized systems. - **How AttestAI helps:** Using Base’s onchain kits, AttestAI integrates wallet functionality, allowing contributors to easily connect, manage their attestations, and track their role in the system securely and efficiently. 4. **Reusable Tools for Developers** - **Why it matters:** Building decentralized apps is complex, and tools that save time and effort can accelerate innovation. - **How AttestAI helps:** It offers modular, reusable components for managing attestations and onchain identity, making it easier for other developers to create secure, decentralized apps on Base.