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Dedicated to the development and integration of the essential functionalities that will drive the vAIn (Virtual Artificial Intelligence Network) decentralized AGI system. Objectives: Federated Learning Engine: Develop and integrate the Federated Learning Engine, enabling nodes to collaboratively train models while preserving data privacy through secure aggregation techniques. Meta-Learning System: Implement the Meta-Learning System, utilizing Model-Agnostic Meta-Learning (MAML) to facilitate cross-domain knowledge transfer and adaptive learning capabilities. Neural Architecture Search: Create the Neural Architecture Search component, employing evolutionary strategies to optimize neural network architectures for improved performance and efficiency. Privacy-Preserving Computation: Integrate privacy-preserving techniques, such as homomorphic encryption and differential privacy, to ensure that sensitive data remains secure during computation and model training. Key Features to Implement: Secure Aggregation: Develop mechanisms for securely aggregating model updates from multiple nodes while maintaining data confidentiality. Node Communication: Implement P2P communication protocols using libp2p to facilitate efficient and secure message passing between nodes. Performance Monitoring: Establish real-time performance monitoring tools to track the efficiency and effectiveness of the federated learning process. User Interface Components: Begin developing the frontend components for the React-based dashboard, allowing users to visualize training progress and node performance.
No due dateFocuses on establishing a robust and scalable architecture for the vAIn (Virtual Artificial Intelligence Network) decentralized AGI system. This milestone aims to outline the foundational components and their interactions within the system, ensuring that the architecture supports the key features of decentralized computation, federated learning, and privacy-preserving technologies. Objectives: Architectural Blueprint: Create a comprehensive architectural diagram that illustrates the core, network, client, and storage layers of the vAIn system. Component Definition: Clearly define the roles and responsibilities of each component within the architecture, including the Federated Learning Engine, Meta-Learning System, and Neural Architecture Search. Project Structure: Establish a well-organized project structure that facilitates collaboration and ease of navigation for developers. This includes defining directories for source code, documentation, and client components. Integration Points: Identify and document integration points between different layers of the architecture, ensuring seamless communication and data flow across the system. Security Considerations: ncorporate security measures into the architecture, including secure message passing, node discovery, and trust scoring mechanisms. Key Components: Core Layer: Focus on the development of the Federated Learning Engine, Meta-Learning System, and privacy-preserving computation techniques. Network Layer: Implement P2P communication protocols using libp2p, ensuring secure and efficient data exchange between nodes. Client Layer: Develop a user-friendly React-based frontend dashboard for real-time monitoring and management of compute nodes. Storage Layer: Set up distributed storage solutions using IPFS for secure data management and model weight distribution.
No due date