Key features
🔹Dynamic Fractional NFTs (dfNFTs) for Computation, AI & Applications
Converts computing resources, AI models, and applications into dfNFTs, enabling fractional ownership, leasing, and efficient execution.
Provides interoperability across decentralized computing environments, ensuring seamless deployment of AI-powered workloads.
🔹Smart Contract-Driven Resource Orchestration
Automates the allocation and execution of AI, applications, and computational tasks.
Uses predictive AI-based scheduling to enhance resource utilization and reduce inefficiencies.
🔹Modular Computation & AI Integration
Supports decentralized execution of AI inference, model training, and dApp services.
Allows applications to access distributed computing resources on a pay-per-use basis.
🔹Cloud-to-Edge AI Computing & Execution
Enables AI & application workloads to dynamically shift between cloud, fog, and edge layers.
Reduces latency for real-time AI model execution and decentralized application processing.
🔹AI-Powered Security & Privacy Enhancements
Use zero-knowledge proof (ZKP) and homomorphic encryption for secure computation.
AI-driven anomaly detection enhances trust and security in decentralized networks.
🔹Decentralized AI, Application & Computation Marketplace
Users can monetize computing, AI models, and application services.
Smart contracts facilitate trustless transactions and execution guarantees.
🔹Decentralized Data Collection & Processing for AI
Data sources are tokenized and secured via smart contracts, ensuring privacy and trust.
AI models access real-time, distributed data for enhanced learning and adaptive intelligence.
Supports privacy-preserving machine learning (PPML), allowing AI models to train without compromising user data security.
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