2.1 Federated Learning Framework and AI Agents

Federated Learning Framework

• Data Privacy and Sharing: Agentora employs federated learning technology, which allows each player's Neuro Agent to retain sensitive data locally while participating in global model training. Model parameters are shared among nodes via encrypted communication without the need to upload raw data centrally, thus safeguarding privacy and enhancing overall intelligence.

• Collaborative Optimization: Under this framework, multiple Neuro Agents can collaboratively optimize to identify cross-player behavioral patterns and market dynamics, thereby providing more precise strategy recommendations and asset management.

• Continuous Learning and Adaptation: As player behavior continues to evolve, the federated learning framework can update models in real-time, ensuring that AI companions remain efficient in personalized asset management, strategy adjustment, and the maintenance of social relationships.

Natively driven by AI Agents

• Neuro Agent:Each player is equipped with an independent AI companion. These intelligent agents continuously learn from user behavior, market changes, and ecosystem feedback to achieve personalized asset custody and strategy optimization.

• Automated Management:The Neuro Agent is not only responsible for monitoring market dynamics and player assets but can also automatically execute operations such as intelligent portfolio rebalancing, risk alerts, and automatic adjustments of social relationship networks. This ensures long-term benefits and ecological participation for players within the game.

• Edge Decision-Making Capability:Leveraging federated learning, each AI Agent can make rapid responses based on local data while maintaining coordination with the global model. This ensures that decisions are both swift and aligned with the overall development direction of the ecosystem.