Federated learning meets blockchain in edge computing: Opportunities and challenges
Mobile-edge computing (MEC) has been envisioned as a promising paradigm to handle the
massive volume of data generated from ubiquitous mobile devices for enabling intelligent …
massive volume of data generated from ubiquitous mobile devices for enabling intelligent …
Distributed learning in wireless networks: Recent progress and future challenges
The next-generation of wireless networks will enable many machine learning (ML) tools and
applications to efficiently analyze various types of data collected by edge devices for …
applications to efficiently analyze various types of data collected by edge devices for …
Edge artificial intelligence for 6G: Vision, enabling technologies, and applications
KB Letaief, Y Shi, J Lu, J Lu - IEEE Journal on Selected Areas …, 2021 - ieeexplore.ieee.org
The thriving of artificial intelligence (AI) applications is driving the further evolution of
wireless networks. It has been envisioned that 6G will be transformative and will …
wireless networks. It has been envisioned that 6G will be transformative and will …
Federated learning for internet of things: Recent advances, taxonomy, and open challenges
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 …
algorithm for both network and application management. However, given the presence of …
Communication-efficient federated learning
Federated learning (FL) enables edge devices, such as Internet of Things devices (eg,
sensors), servers, and institutions (eg, hospitals), to collaboratively train a machine learning …
sensors), servers, and institutions (eg, hospitals), to collaboratively train a machine learning …
From distributed machine learning to federated learning: A survey
In recent years, data and computing resources are typically distributed in the devices of end
users, various regions or organizations. Because of laws or regulations, the distributed data …
users, various regions or organizations. Because of laws or regulations, the distributed data …
FedCPF: An efficient-communication federated learning approach for vehicular edge computing in 6G communication networks
The sixth-generation network (6G) is expected to achieve a fully connected world, which
makes full use of a large amount of sensitive data. Federated Learning (FL) is an emerging …
makes full use of a large amount of sensitive data. Federated Learning (FL) is an emerging …
Over-the-air federated learning from heterogeneous data
T Sery, N Shlezinger, K Cohen… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
We focus on over-the-air (OTA) Federated Learning (FL), which has been suggested
recently to reduce the communication overhead of FL due to the repeated transmissions of …
recently to reduce the communication overhead of FL due to the repeated transmissions of …
Communication-efficient and distributed learning over wireless networks: Principles and applications
Machine learning (ML) is a promising enabler for the fifth-generation (5G) communication
systems and beyond. By imbuing intelligence into the network edge, edge nodes can …
systems and beyond. By imbuing intelligence into the network edge, edge nodes can …
Federated learning: A signal processing perspective
The dramatic success of deep learning is largely due to the availability of data. Data
samples are often acquired on edge devices, such as smartphones, vehicles, and sensors …
samples are often acquired on edge devices, such as smartphones, vehicles, and sensors …