Computer implemented multi-layer federated learning method based on distributed clustering and non-transitory computer readable medium thereof

Abstract

A multi-layer federated learning method based on distributed clustering is provided, which comprises the following steps. Computing a feature distribution similarity for each of participating nodes with non-(non-independent and identically distributed) data sets, and grouping these nodes into plural clusters by the feature distribution similarity. Updating local model of nodes of each cluster by a federated learning algorithm, and inputting these nodes into a multi-layer aggregation mechanism. Terminating the operation of the multi-layer aggregation mechanism until the clustering result meets a required demand. Furthermore, we implement a blockchain-based multi-layer federated learning system, including model aggregation module, API module, time-series synchronization module, and IPFS, based on distributed clustering architecture. The learning performance is proven to effectively improved.