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 …
MA Lahmeri, MA Kishk… - IEEE Open Journal of the …, 2021 - ieeexplore.ieee.org
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 …
D Liu, H Kong, X Luo, W Liu, R Subramaniam - Neurocomputing, 2022 - Elsevier
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 …