Abstract Edge Artificial Intelligence (AI) incorporates a network of interconnected systems
and devices that receive, cache, process, and analyse data in close communication with the …

Segment Anything Model (SAM) has attracted significant attention due to its impressive zero-
shot transfer performance and high versatility for numerous vision applications (like image …

Segment anything model (SAM) developed by Meta AI Research has recently attracted
significant attention. Trained on a large segmentation dataset of over 1 billion masks, SAM is …

Segment anything model (SAM), as the name suggests, is claimed to be capable of cutting
out any object and demonstrates impressive zero-shot transfer performance with the …

Abstract The Segment Anything model (SAM) has brought significant changes to the
segmentation field with its superior performance, but its extensive computational resource …

One big challenge to federated learning is the non-IID data distribution caused by
imbalanced classes. Existing federated learning approaches tend to bias towards classes …

Federated learning (FL) is a privacy-preserving distributed framework for collaborative
model training in edge networks. However, challenges such as vulnerability to adversarial …

Federated learning (FL) is a privacy-preserving distributed management framework based
on collaborative model training of distributed devices in edge networks. However, recent …

Federated Learning (FL) has emerged as a promising paradigm for collaborative model
training across distributed clients while preserving data privacy. However, prevailing FL …

Federated learning (FL) makes it possible to advance towards edge intelligence by enabling
collaborative and privacy-preserving model training across distributed edge devices. One of …