The emergence of the Internet of Things (IoT) signifies a transformative wave of innovation,
establishing a network of devices designed to enrich everyday experiences. Developing …

With the rapid growth in the volume of data sets, models, and devices in the domain of deep
learning, there is increasing attention on large-scale distributed deep learning. In contrast to …

With billions of Internet of Things devices producing vast data globally, privacy and efficiency
challenges arise in artificial intelligence applications. Federated learning (FL) has been …

Federated Learning has emerged as a revolutionary technology in Machine Learning (ML),
enabling collaborative training of models in a distributed environment while ensuring privacy …

Federated Learning (FL) enables distributed clients to collaboratively train models without
exposing their private data. However, it is difficult to implement efficient FL due to limited …

Federated Learning (FL) enables multiple clients to collaboratively train a model without
sharing their local data. Yet the FL system is vulnerable to well-designed Byzantine attacks …

In edge computing (EC), federated learning (FL) enables numerous distributed devices (or
workers) to collaboratively train AI models without exposing their local data. Most works of …

The massive data from mobile and embedded devices have huge potential for training
machine learning models. Decentralized machine learning (DML) can avoid the inherent …

Federated learning (FL) is an efficient distributed learning method that facilitates
collaborative model training among multiple edge devices (or clients). However, current …

To provide a flexible tradeoff between inference accuracy and resource requirement at
runtime, the slimmable neural network (SNN), a single network executable at different widths …