Federated learning (FL) aims to collaboratively train a global model while ensuring client data privacy. However, FL faces challenges from the non-IID data distribution among clients. Clustered FL (CFL) has emerged as a promising solution, but most existing CFL frameworks adopt synchronous frameworks lacking asynchrony. An asynchronous CFL framework called SDAGFL based on directed acyclic graph distributed ledger techniques (DAG-DLT) was proposed, but its complete decentralization leads to high communication and storage costs. We propose DAG-ACFL, an asynchronous clustered FL framework based on directed acyclic graph distributed ledger techniques (DAG-DLT). We first detail the components of DAG-ACFL. A tip selection algorithm based on the cosine similarity of model parameters is then designed to aggregate models from clients with similar distributions. An adaptive tip selection algorithm leveraging change-point detection dynamically determines the number of selected tips. We evaluate the clustering and training performance of DAG-ACFL on multiple datasets and analyze its communication and storage costs. Experiments show the superiority of DAG-ACFL in asynchronous clustered FL. By combining DAG-DLT with clustered FL, DAG-ACFL realizes robust, decentralized and private model training with efficient performance.

以基于有向无环图分布式账本技术（DAG-DLT）的异步聚类联邦学习（CFL）框架为基础，提出了一种名为DAG-ACFL的异步聚类联邦学习框架，通过组合DAG-DLT和聚类FL，DAG-ACFL实现了强大的、去中心化的、私密的模型训练。

DAG-ACFL: 基于DAG-DLT的异步聚类联邦学习