Method and System for edge intelligence using federated learning with blockchain, covariance matrix transfer, and artificial intelligence (FLwBC-AI)

原文（中文）

This disclosure describes methods for distributed machine learning across edge nodes. An edge node acquires new data and checks with a federated server for an updated large language model (LLM) or small learning model (SLM). If available, it downloads the update; otherwise, it uses the existing model. The edge node then trains the LLM/SLM on its new data and sends the updated model back to the server. This enables continuous, privacy-preserving model improvement with low communication overhead. Additionally, methods are provided for sequential task execution at the edge: a smart contract provides a pointer to a covariance matrix, which is loaded into a neural network. The edge node processes sensor data to generate a task output, then repeats the process for subsequent tasks using new matrices and updated models as needed.