Decentralizing AI: A Guide to Building Scalable and Secure Decentralized AI Platforms
Decentralizing AI: A Guide to Building Scalable and Secure Decentralized AI Platforms Note: This guide is based on research from decentralized AI projects (Ocean Protocol, Fetch.ai, SingularityNET), federated learning frameworks (Flower, PySyft), and academic papers on privacy-preserving machine learning. Code examples are derived from official documentation and community implementations. Decentralized AI addresses fundamental challenges in traditional centralized AI systems: data privacy, model ownership, computational bottlenecks, and single points of failure. According to research from the IEEE and ACM, decentralized AI encompasses three primary approaches: federated learning (training on distributed data without centralization), blockchain-based model registries (transparent model provenance), and distributed inference (computational load distribution). ...