FedCMR: A Library for Federated Continual Model Refinement

Xiaoyang Wang, Chaoyang He, Salman Avestimehr, Mahdi Marsousi

Keywords: Continual model refinement, MLOps, federated learning

TL;DR: We present a library for continual model refinement tasks in federated learning settings.

Abstract: Machine learning models suffer from performance degradation when out-of-distribution (OOD) data samples, which do not come from their training data distributions, emerge after model deployment. A common practice called continual model refinement (CMR) in machine learning operations (MLOps) can alleviate such performance degradation by continuously refining deployed models over OOD data samples. However, few existing works on CMR tasks have considered federated learning (FL) settings where the OOD data samples are ubiquitous. To support CMR tasks in federated learning scenarios, we present a library called FedCMR, which includes a holistic pipeline that enables end-to-end CMR task evaluation ranging from data selection and labeling to model refinement and evaluation. We further show a case of integrating FedCMR with a federated learning ecosystem backed by the FedML production system (He et al., 2020). We hope that FedCMR could provide an efficient means for developing and evaluating federated CMR algorithms. We will open-source our library upon publication.