4.6 Edge Computing and Distributed AI

AISim's application in edge computing mainly involves deploying lightweight AI models at edge nodes to provide low-latency, high-efficiency real-time computing support. Since these edge nodes process data locally, they can reduce the latency brought by traditional cloud computing, ensuring that tasks such as autonomous driving and smart medical care can respond in real-time, improving the overall efficiency of the system.

Privacy-protected distributed training is another highlight of AISim in edge computing. AI Agents collaborate to train models on multiple devices using federated learning and other technologies without directly exchanging sensitive data. This not only protects user privacy but also ensures the security of data during distributed training.

AISim's AI Agents also support dynamic task allocation. Based on the current network status and task priority, AI Agents can intelligently distribute computing tasks between edge nodes and the cloud. When network load is low, tasks can be allocated to the cloud; during network congestion, tasks are prioritized for processing by edge nodes, ensuring efficient task execution.