Federated Learning (FL) is an emerging paradigm that enables multiple users
to collaboratively train a robust model in a privacy-preserving manner without
sharing their private data. Most existing approaches of FL only consider
traditional single-label image classification, ignoring the impact when
transferring the task to multi-label image classification. Nevertheless, it is
still challenging for FL to deal with user heterogeneity in their local data
distribution in the real-world FL scenario, and this issue becomes even more
severe in multi-label image classification. Inspired by the recent success of
Transformers in centralized settings, we propose a novel FL framework for
multi-label classification. Since partial label correlation may be observed by
local clients during training, direct aggregation of locally updated models
would not produce satisfactory performances. Thus, we propose a novel FL
framework of Language-Guided Transformer (FedLGT) to tackle this challenging
task, which aims to exploit and transfer knowledge across different clients for
learning a robust global model. Through extensive experiments on various
multi-label datasets (e.g., FLAIR, MS-COCO, etc.), we show that our FedLGT is
able to achieve satisfactory performance and outperforms standard FL techniques
under multi-label FL scenarios. Code is available at
this https URL

提出了一种用于多标签分类的新型联邦学习框架（FedLGT），旨在解决用户异构性和标签相关性等挑战，通过在不同客户端之间传递知识来学习出稳健的全局模型，实验证明在多标签联邦学习场景下超过了标准联邦学习技术，并获得了令人满意的性能。