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Unleashing the power of edge-cloud generative AI in mobile networks: A survey of AIGC services
Artificial Intelligence-Generated Content (AIGC) is an automated method for generating,
manipulating, and modifying valuable and diverse data using AI algorithms creatively. This …
manipulating, and modifying valuable and diverse data using AI algorithms creatively. This …
A survey on the convergence of edge computing and AI for UAVs: Opportunities and challenges
P McEnroe, S Wang, M Liyanage - IEEE Internet of Things …, 2022 - ieeexplore.ieee.org
The latest 5G mobile networks have enabled many exciting Internet of Things (IoT)
applications that employ unmanned aerial vehicles (UAVs/drones). The success of most …
applications that employ unmanned aerial vehicles (UAVs/drones). The success of most …
Recent advances on federated learning for cybersecurity and cybersecurity for federated learning for internet of things
Decentralized paradigm in the field of cybersecurity and machine learning (ML) for the
emerging Internet of Things (IoT) has gained a lot of attention from the government …
emerging Internet of Things (IoT) has gained a lot of attention from the government …
Client selection in federated learning: Principles, challenges, and opportunities
As a privacy-preserving paradigm for training machine learning (ML) models, federated
learning (FL) has received tremendous attention from both industry and academia. In a …
learning (FL) has received tremendous attention from both industry and academia. In a …
Homomorphic encryption-based privacy-preserving federated learning in IoT-enabled healthcare system
L Zhang, J Xu, P Vijayakumar… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
In this work, the federated learning mechanism is introduced into the deep learning of
medical models in Internet of Things (IoT)-based healthcare system. Cryptographic …
medical models in Internet of Things (IoT)-based healthcare system. Cryptographic …
Oort: Efficient federated learning via guided participant selection
F Lai, X Zhu, HV Madhyastha… - 15th {USENIX} Symposium …, 2021 - usenix.org
Federated Learning (FL) is an emerging direction in distributed machine learning (ML) that
enables in-situ model training and testing on edge data. Despite having the same end goals …
enables in-situ model training and testing on edge data. Despite having the same end goals …
Federated learning in mobile edge networks: A comprehensive survey
In recent years, mobile devices are equipped with increasingly advanced sensing and
computing capabilities. Coupled with advancements in Deep Learning (DL), this opens up …
computing capabilities. Coupled with advancements in Deep Learning (DL), this opens up …
A survey on distributed machine learning
J Verbraeken, M Wolting, J Katzy… - Acm computing surveys …, 2020 - dl.acm.org
The demand for artificial intelligence has grown significantly over the past decade, and this
growth has been fueled by advances in machine learning techniques and the ability to …
growth has been fueled by advances in machine learning techniques and the ability to …
The limitations of federated learning in sybil settings
C Fung, CJM Yoon, I Beschastnikh - 23rd International Symposium on …, 2020 - usenix.org
Federated learning over distributed multi-party data is an emerging paradigm that iteratively
aggregates updates from a group of devices to train a globally shared model. Relying on a …
aggregates updates from a group of devices to train a globally shared model. Relying on a …
Deep learning with edge computing: A review
Deep learning is currently widely used in a variety of applications, including computer vision
and natural language processing. End devices, such as smartphones and Internet-of-Things …
and natural language processing. End devices, such as smartphones and Internet-of-Things …