Transformer architectures are highly expressive because they use self-attention
mechanisms to encode long-range dependencies in the input sequences. In this paper, we …

Federated learning enables collaborative training of a model while kee** the training data
decentralized and private. However, in IoT systems, inherent heterogeneity in processing …

The rapid advancement of Internet of Things (IoT) technology has transformed the digital
landscape, enabling unprecedented connectivity between devices, people, and services …

The reliance of language model training on massive amounts of computation and vast
datasets scraped from potentially low-quality, copyrighted, or sensitive data has come into …

Currently, the federated graph neural network (GNN) has attracted a lot of attention due to its
wide applications in reality without violating the privacy regulations. Among all the privacy …

Domain generalization (DG) aims to learn from multiple known source domains a model that
can generalize well to unknown target domains. The existing DG methods usually exploit the …

The emerging integration of Internet of Things (IoT) and AI has unlocked numerous
opportunities for innovation across diverse industries. However, growing privacy concerns …

Meeting summarization has emerged as a promising technique for providing users with
condensed summaries. However, existing work has focused on training models on …

Edge Computing (EC) has gained significant traction in recent years, promising enhanced
efficiency by integrating Artificial Intelligence (AI) capabilities at the edge. While the focus …

Federated learning (FL) is a decentralized machine learning (ML) framework that allows
models to be trained without sharing the participants' local data. FL thus preserves privacy …