The Internet of Things (IoT) is penetrating many facets of our daily life with the proliferation of
intelligent services and applications empowered by artificial intelligence (AI). Traditionally …

Federated learning (FL) is a distributed machine learning strategy that generates a global
model by learning from multiple decentralized edge clients. FL enables on-device training …

The advancements in machine learning (ML) opened a new opportunity to bring intelligence
to the low-end Internet-of-Things (IoT) nodes, such as microcontrollers. Conventional ML …

Abstract Online Social Networks are perpetually evolving and used in plenteous
applications such as content sharing, chatting, making friends/followers, customer …

This paper presents a simple yet effective approach that improves continual test-time
adaptation (TTA) in a memory-efficient manner. TTA may primarily be conducted on edge …

Federated machine unlearning (FMU) aims to remove the influence of a specified subset of
training data upon request from a trained federated learning model. Despite achieving …

Unmanned aerial vehicles (UAVs) are considered as one of the promising technologies for
the next-generation wireless communication networks. Their mobility and their ability to …

Modern networks generate a massive amount of traffic data streams. Analyzing this data is
essential for various purposes, such as network resources management and cyber-security …

Edge computing and artificial intelligence (AI), especially deep learning algorithms, are
gradually intersecting to build the novel system, namely edge intelligence. However, the …

With an increasing number of smart devices like internet of things devices deployed in the
field, offloading training of neural networks (NNs) to a central server becomes more and …