Federated learning (FL) is a promising machine learning paradigm that collaborates with
client models to capture global knowledge. However, deploying FL models in real-world …

Asynchronous federated learning, which enables local clients to send their model update
asynchronously to the server without waiting for others, has recently emerged for its …

Federated Continual Learning (FCL) has emerged as a robust solution for collaborative
model training in dynamic environments, where data samples are continuously generated …

In personalized Federated Learning (pFL), high data heterogeneity can cause significant
gradient divergence across devices, adversely affecting the learning process. This …

Federated learning is a popular machine learning paradigm that achieves decentralized
model training on distributed devices, ensuring data decentralization, privacy protection, and …

Vertical Federated Learning (VFL) has emerged as a crucial privacy-preserving learning
paradigm that involves training models using distributed features from shared samples …

Domain Generalization (DG) aims to learn from multiple known source domains a model that
can generalize well to unknown target domains. One of the key approaches in DG is training …

Federated Learning (FL) is a promising distributed machine learning framework that
emphasizes privacy protection. However, inconsistencies between local optimization …

When dealing with non-IID data, federated learning confronts issues such as client drift and
sluggish convergence. Therefore, we propose a Bidirectional Corrective Model-Contrastive …

Data exhibit the distribution of the problem space, and the efficacy of machine learning
models is contingent upon the availability of quality datasets. Additionally, in traditional …