Distributed AI refers to artificial intelligence systems in which data processing, model execution, or decision-making is spread across multiple connected devices, machines, servers, or locations rather than being handled by a single centralized system. These components can work together by sharing information, computations, or model updates.
For example, a fleet of connected vehicles can process sensor data locally to detect obstacles while sharing selected insights with other vehicles or a central platform. This approach can reduce latency, improve scalability, support real-time decisions, and keep sensitive data closer to where it is generated. Distributed AI is commonly used in edge computing, IoT, robotics, autonomous systems, and large-scale machine learning. Related concepts include federated learning, edge AI, multi-agent systems, decentralized AI, and distributed computing.
