The Internet of Things (IoT) will be ripe for the deployment of novel machine learning
algorithm for both network and application management. However, given the presence of …

The rapid development of the Internet and smart devices trigger surge in network traffic
making its infrastructure more complex and heterogeneous. The predominated usage of …

Federated Learning (FL) is an increasingly popular form of distributed machine learning that
addresses privacy concerns by allowing participants to collaboratively train machine …

Federated learning (FL) offers distributed machine learning on edge devices. However, the
FL model raises privacy concerns. Various techniques, such as homomorphic encryption …

Deep learning (DL) has demonstrated great performance in various applications on
powerful computers and servers. Recently, with the advancement of more powerful mobile …

Abstract Machine learning has revolutionized every facet of human life, while also becoming
more accessible and ubiquitous. Its prevalence has had a powerful impact in healthcare …

Vertical federated learning (VFL) is a promising category of federated learning for the
scenario where data is vertically partitioned and distributed among parties. VFL enriches the …

Federated Learning (FL) is widely used in various industries because it effectively
addresses the predicament of isolated data island. However, eavesdroppers is capable of …

Cross-silo federated learning (FL) adopts various cryptographic operations to preserve data
privacy, which introduces significant performance overhead. In this paper, we identify nine …

Federated learning (FL) technology has emerged for efficient data collection, data privacy
protection, and efficient utilization of computing resources. In FL-based systems, data …