CLIP-guided Federated Learning on Heterogeneous and Long-Tailed Data
Jiangming Shi Shanshan Zheng Xiangbo Yin Yang Lu Yuan Xie Yanyun Qu
Federated learning (FL) provides a decentralized machine learning paradigm
where a server collaborates with a group of clients to learn a global model
without accessing the clients' data. User heterogeneity is a significant
challenge for FL, which together with the class-distribution imbalance further
enhances the difficulty of FL. Great progress has been made in large
vision-language models, such as Contrastive Language-Image Pre-training (CLIP),
which paves a new way for image classification and object recognition. Inspired
by the success of CLIP on few-shot and zero-shot learning, we use CLIP to
optimize the federated learning between server and client models under its
vision-language supervision. It is promising to mitigate the user heterogeneity
and class-distribution balance due to the powerful cross-modality
representation and rich open-vocabulary prior knowledge. In this paper, we
propose the CLIP-guided FL (CLIP2FL) method on heterogeneous and long-tailed
data. In CLIP2FL, the knowledge of the off-the-shelf CLIP model is transferred
to the client-server models, and a bridge is built between the client and
server. Specifically, for client-side learning, knowledge distillation is
conducted between client models and CLIP to improve the ability of client-side
feature representation. For server-side learning, in order to mitigate the
heterogeneity and class-distribution imbalance, we generate federated features
to retrain the server model. A prototype contrastive learning with the
supervision of the text encoder of CLIP is introduced to generate federated
features depending on the client-side gradients, and they are used to retrain a
balanced server classifier.