Federated Class Incremental Learning Method With High Accuracy and Extremely Low Communication Cost Based on Broad Learning System

Federated Class Incremental Learning (FCIL) enables distributed clients to collaboratively train a global model based on their private sequential tasks without compromising data privacy. Currently, some FCIL methods have been proposed, and most are designed based on deep models. However, enabling these FCIL models to converge requires numerous communication rounds, significantly increasing communication costs. Recently, the Broad Learning System (BLS), an effective and efficient shallow model, was proposed and adapted for CIL tasks [i.e., BLS-Class Incremental Learning (CIL)]. BLS-CIL exhibits fast updates and high retainability. However, it requires prior knowledge of when new class data arrives and cannot be directly used in federated scenarios due to the global catastrophic forgetting in FCIL. Thus, an innovative Federated Class incremental learning method based on BLS (FedCBLS) is proposed, which extends BLS-CIL within the federated scenario and provides three advantages: 1) high accuracy from the local perspective, achieved by integrating BLS-CIL with a newly designed automatic decision-making (ADM) method to detect novel classes and learn them incrementally for local clients; 2) high accuracy from the global perspective, attained through the newly proposed local model refinement (LMR) and global model projection (GMP) methods, mitigating global catastrophic forgetting stemming from heterogeneous data across clients; and 3) extremely low communication costs due to the newly derived closed-form solutions without iterative optimization for both local and global models. Comprehensive experimental results show that our FedCBLS outperforms the state-of-the-art (SOTA) FCIL methods by up to 8.15%, while drastically reducing communication costs to 1% of SOTA’s. Our code is available athttps://github.com/dujie-szu/FedCBLS.git