FedCV: A Federated Learning Framework for Diverse Computer Vision Tasks

TL;DR

FedCV introduces a comprehensive federated learning framework and benchmark for diverse computer vision tasks, addressing challenges like dataset heterogeneity and system efficiency to advance FL research in this domain.

Contribution

The paper presents FedCV, a unified library and benchmarking platform for FL in computer vision, covering classification, segmentation, and detection tasks with diverse datasets and algorithms.

Findings

Non-I.I.D. datasets reduce model accuracy.

Centralized training tricks are not directly applicable to FL.

System efficiency remains a challenge due to large models and memory costs.

Abstract

Federated Learning (FL) is a distributed learning paradigm that can learn a global or personalized model from decentralized datasets on edge devices. However, in the computer vision domain, model performance in FL is far behind centralized training due to the lack of exploration in diverse tasks with a unified FL framework. FL has rarely been demonstrated effectively in advanced computer vision tasks such as object detection and image segmentation. To bridge the gap and facilitate the development of FL for computer vision tasks, in this work, we propose a federated learning library and benchmarking framework, named FedCV, to evaluate FL on the three most representative computer vision tasks: image classification, image segmentation, and object detection. We provide non-I.I.D. benchmarking datasets, models, and various reference FL algorithms. Our benchmark study suggests that there are multiple challenges that deserve future exploration: centralized training tricks may not be directly applied to FL; the non-I.I.D. dataset actually downgrades the model accuracy to some degree in different tasks; improving the system efficiency of federated training is challenging given the huge number of parameters and the per-client memory cost. We believe that such a library and benchmark, along with comparable evaluation settings, is necessary to make meaningful progress in FL on computer vision tasks. FedCV is publicly available: https://github.com/FedML-AI/FedCV.