Decentralized federated learning: Fundamentals, state of the art, frameworks, trends, and challenges
In recent years, Federated Learning (FL) has gained relevance in training collaborative
models without sharing sensitive data. Since its birth, Centralized FL (CFL) has been the …
models without sharing sensitive data. Since its birth, Centralized FL (CFL) has been the …
Combining federated learning and edge computing toward ubiquitous intelligence in 6G network: Challenges, recent advances, and future directions
Full leverage of the huge volume of data generated on a large number of user devices for
providing intelligent services in the 6G network calls for Ubiquitous Intelligence (UI). A key to …
providing intelligent services in the 6G network calls for Ubiquitous Intelligence (UI). A key to …
Edge learning for B5G networks with distributed signal processing: Semantic communication, edge computing, and wireless sensing
To process and transfer large amounts of data in emerging wireless services, it has become
increasingly appealing to exploit distributed data communication and learning. Specifically …
increasingly appealing to exploit distributed data communication and learning. Specifically …
Nine challenges in artificial intelligence and wireless communications for 6G
W Tong, GY Li - IEEE Wireless Communications, 2022 - ieeexplore.ieee.org
In recent years, artificial intelligence (AI) techniques, especially machine learning (ML), have
been successfully applied in various areas, leading to a widespread belief that AI will …
been successfully applied in various areas, leading to a widespread belief that AI will …
Federated learning via inexact ADMM
One of the crucial issues in federated learning is how to develop efficient optimization
algorithms. Most of the current ones require full device participation and/or impose strong …
algorithms. Most of the current ones require full device participation and/or impose strong …
Towards efficient communications in federated learning: A contemporary survey
In the traditional distributed machine learning scenario, the user's private data is transmitted
between clients and a central server, which results in significant potential privacy risks. In …
between clients and a central server, which results in significant potential privacy risks. In …
Exploring representativity in device scheduling for wireless federated learning
Z Chen, W Yi, A Nallanathan - IEEE Transactions on Wireless …, 2023 - ieeexplore.ieee.org
Existing device scheduling works in wireless federated learning (FL) mainly focused on
selecting the devices with maximum gradient norm or loss function and require all devices to …
selecting the devices with maximum gradient norm or loss function and require all devices to …
GoMORE: Global model reuse for resource-constrained wireless federated learning
Due to the dynamics of wireless channels and limited wireless resources (ie, spectrum),
deploying federated learning (FL) over wireless networks is challenged by frequent FL …
deploying federated learning (FL) over wireless networks is challenged by frequent FL …
Communication-efficient ADMM-based federated learning
Federated learning has shown its advances over the last few years but is facing many
challenges, such as how algorithms save communication resources, how they reduce …
challenges, such as how algorithms save communication resources, how they reduce …
Data and Model Poisoning Backdoor Attacks on Wireless Federated Learning, and the Defense Mechanisms: A Comprehensive Survey
Due to the greatly improved capabilities of devices, massive data, and increasing concern
about data privacy, Federated Learning (FL) has been increasingly considered for …
about data privacy, Federated Learning (FL) has been increasingly considered for …