Federated Learning Benchmarking for Computer Vision Tasks
FedCV: A Federated Learning Framework for Diverse Computer Vision Tasks
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.
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 objectdetection.
We provide non-i.d.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.d.d.d.
Dataset actually downgrades the modelaccuracy 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.
Chaoyang He, Alay Dilipbhai Shah, Zhenheng Tang, Di Fan1Adarshan Naiynar Sivashunmugam, Keerti Bhogaraju, Mita Shimpi, Li Shen, Xiaowen Chu, Mahdi Soltanolkotabi, Salman Avestimehr