Federated Graph Learning is a privacy-preserving collaborative approach for training a
shared model on graph-structured data in the distributed environment. However, in real …

Federated learning (FL) has emerged as a secure paradigm for collaborative training among
clients. Without data centralization, FL allows clients to share local information in a privacy …

Abstract Multi-label Intention Understanding (MIU) for images is a critical yet challenging
domain, primarily due to the ambiguity of intentions leading to a resource-intensive …

Federated learning presents massive potential for privacy-friendly vision task collaboration.
However, the federated visual performance is deeply affected by backdoor attacks, where …

Prevalent federated learning commonly develops under the assumption that the ideal global
class distributions are balanced. In contrast, real-world data typically follows the long-tailed …

Wildlife ReID involves utilizing visual technology to identify specific individuals of wild
animals in different scenarios, holding significant importance for wildlife conservation …

Personalized Federated Graph Learning (pFGL) facilitates the decentralized training of
Graph Neural Networks (GNNs) without compromising privacy while accommodating …

Federated learning (FL) has surged in prominence due to its capability of collaborative
model training without direct data sharing. However, the vast disparity in local data …

Abstract Federated Domain Generalization aims to learn a domain-invariant model from
multiple decentralized source domains for deployment on unseen target domain. Due to …

Federated learning (FL) has emerged as a promising paradigm for training segmentation
models on decentralized medical data, owing to its privacy-preserving property. However …