Swarm Cognition Networks: A Novel Self-Evolving AI Architecture for Decentralized Intelligence
Abstract
Traditional AI models face challenges in decentralized environments due to scalability and adaptability constraints. This paper introduces Swarm Cognition Networks (SCN), a novel AI paradigm inspired by biological swarm intelligence and collective cognition. SCN integrates reinforcement learning with dynamic multi-agent communication protocols, enabling self-evolving decision-making in distributed systems. Through real-world simulations in IoT and edge computing environments, SCN demonstrates improved efficiency, fault tolerance, and adaptability over conventional federated learning approaches. This work sets a foundation for future AI systems capable of decentralized, self-optimizing intelligence.
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