HONORING OUR CONTRIBUTORS

Validator and sustainability means Roko is not just running tests; we’re in the throes of an extensive validation process. This involves deploying our own validators/miners and rigorously assessing every aspect to guarantee their reliability under various scenarios. It’s a monumental task, given the complexity and specialized nature of our project. These are not challenges that can be swiftly addressed by pulling in a strategic DePin partner. Timing and onboarding will be realisticly around end April>early May.

Roko Tokenomics R&D 9/18 20hrs

GPU’s purchased, Mac Mini’s purchased, Tao Validator and mining coming, .box domain, Legal being setup, deving a payments protocol for LLM’s, robot arm and roko box moving along with public updates. Treasury holdings in TAO, Marlin, Syntropy and 21e8 Macs.. Some serious web 3.0 building blocks.  Be paying Roko for an agent to run a homemade robot bye end of year, is my guess.

• Ongoing communication: The team is currently in continuous dialogue with their legal department or legal advisors. This dialogue is  concerning the project’s direction and legal considerations.
• DeFi strategy consideration: They are considering how implementing a DeFi strategy could affect the project’s structure. However, this strategy is still in the conceptual phase, meaning it’s in the early stages of planning and has not been fully developed or implemented yet.
• Focus on foundation setup: The immediate focus is on establishing a foundation. This foundation aims to officially receive grants, which are financial contributions that support the project. The urgency implies that setting up this foundation is crucial for the project’s next steps and long-term sustainability.
• Separate legal entity for asset and intellectual property protection: The priority is to have a legal structure (a “shield”) that separates assets and intellectual property (IP) from the current operating business, particularly the business working on “Roko.” This separation is likely intended to protect these assets from any operational risks or liabilities the business might face.
• Transition towards a DAO-controlled foundation: Lastly, there is a goal to transition the foundation towards being controlled by a Decentralized Autonomous Organization (DAO). A DAO is an organization represented by rules encoded as a computer program that is transparent, controlled by the organization members, and not influenced by a central government. This indicates an ambition to have a more decentralized and member-driven governance structure for the foundation in the future.
• integrating DeFi into a broader project, emphasizing legal considerations, structural setup, and future governance models.

Federated learning is a machine learning approach where a global model is trained across decentralized devices, such as robots, without centralizing raw data. Each device contributes insights from its local data to improve the global model collaboratively.

How Does Federated Learning Work with Robot LLCs?

In federated learning, robot LLCs act as decentralized nodes that train local models using their own data. They collaborate with a central coordinating entity, often a DAO, to contribute to the training of a global model while maintaining data privacy.

providing feedback, proposing improvements, and participating in decision-making processes within the DAO. Transparent governance ensures alignment with community values and objectives.

Decentralized oracles are mechanisms that fetch and deliver real-world data onto a blockchain or decentralized network in a trustless and secure manner. They provide external information to smart contracts or applications, enabling informed decision-making.

Decentralized oracles provide external information, such as market data or weather conditions, that can influence decision-making in the federated learning process. This enhances the adaptability and effectiveness of the global model trained across decentralized devices.

What Role Does the DAO Play in Federated Learning?

The DAO (Decentralized Autonomous Organization) serves as the coordinating entity for federated learning, managing governance and decision-making processes. It integrates model updates from decentralized devices, aggregates insights, and adjusts global model parameters.

How Do Robot LLCs Protect Data Privacy in Federated Learning?

Robot’s will have individual LLCs with Erbit id’s?*CHECK*) ensure data privacy by training local models on their own datasets without centralizing raw data. They only communicate aggregated model updates to the DAO, preserving the privacy of individual data points.

Can I Participate in Federated Learning as a Community Member?

Yes, community members can participate in federated learning by contributing devices or resources to the decentralized $roko network. Participation may involve holding tokens, voting on governance decisions, contributing a form of compute power, or providing feedback on the learning process.

What Privacy Measures Are in Place for Federated Learning?nFederated learning employs privacy-preserving techniques, such as differential privacy and secure multi-party computation, to protect raw data during model training. These measures ensure that individual data remains confidential while contributing to model improvement.

How Are Model Updates Aggregated in Federated Learning?

Model updates from decentralized devices, including robot LLCs, are aggregated at the DAO level using decentralized consensus mechanisms. Techniques like federated averaging combine updates to create an improved global model across the entire $roko network.

What Is the Community’s Role in Governing Federated Learning?

The $roko community plays a vital role in governing federated learning by

Decentralized oracles provide external information, such as market data or weather conditions, that can influence decision-making in the federated learning process. This enhances the adaptability and effectiveness of the global model trained across decentralized devices.

The DAO (Decentralized Autonomous Organization) serves as the coordinating entity for federated learning, managing governance and decision-making processes. It integrates model updates from decentralized devices, aggregates insights, and adjusts global model parameters.

Robot’s will have individual LLCs with Erbit id’s?*CHECK*) ensure data privacy by training local models on their own datasets without centralizing raw data. They only communicate aggregated model updates to the DAO, preserving the privacy of individual data points.

Yes, community members can participate in federated learning by contributing devices or resources to the decentralized $roko network. Participation may involve holding tokens, voting on governance decisions, contributing a form of compute power, or providing feedback on the learning process.

Federated learning employs privacy-preserving techniques, such as differential privacy and secure multi-party computation, to protect raw data during model training. These measures ensure that individual data remains confidential while contributing to model improvement.

How Are Model Updates Aggregated in Federated Learning?

Model updates from decentralized devices, including robot LLCs, are aggregated at the DAO level using decentralized consensus mechanisms. Techniques like federated averaging combine updates to create an improved global model across the entire $roko network.

The $roko community plays a vital role in governing federated learning by providing feedback, proposing improvements, and participating in decision-making processes within the DAO. Transparent governance ensures alignment with community values and objectives.