DAG-ACFL: Asynchronous Clustered Federated Learning based on DAG-DLT
Xiaofeng Xue Haokun Mao Qiong Li
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.