A survey on federated learning for resource-constrained IoT devices
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 …
model by learning from multiple decentralized edge clients. FL enables on-device training …
Federated optimization in heterogeneous networks
Federated Learning is a distributed learning paradigm with two key challenges that
differentiate it from traditional distributed optimization:(1) significant variability in terms of the …
differentiate it from traditional distributed optimization:(1) significant variability in terms of the …
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 …
Adaptive federated learning in resource constrained edge computing systems
Emerging technologies and applications including Internet of Things, social networking, and
crowd-sourcing generate large amounts of data at the network edge. Machine learning …
crowd-sourcing generate large amounts of data at the network edge. Machine learning …
Federated optimization: Distributed machine learning for on-device intelligence
We introduce a new and increasingly relevant setting for distributed optimization in machine
learning, where the data defining the optimization are unevenly distributed over an …
learning, where the data defining the optimization are unevenly distributed over an …
Federated learning over wireless networks: Optimization model design and analysis
There is an increasing interest in a new machine learning technique called Federated
Learning, in which the model training is distributed over mobile user equipments (UEs), and …
Learning, in which the model training is distributed over mobile user equipments (UEs), and …
Low-latency federated learning and blockchain for edge association in digital twin empowered 6G networks
Emerging technologies, such as digital twins and 6th generation (6G) mobile networks, have
accelerated the realization of edge intelligence in industrial Internet of Things (IIoT). The …
accelerated the realization of edge intelligence in industrial Internet of Things (IIoT). The …
Scheduling policies for federated learning in wireless networks
Motivated by the increasing computational capacity of wireless user equipments (UEs), eg,
smart phones, tablets, or vehicles, as well as the increasing concerns about sharing private …
smart phones, tablets, or vehicles, as well as the increasing concerns about sharing private …
fPINNs: Fractional physics-informed neural networks
Physics-informed neural networks (PINNs), introduced in M. Raissi, P. Perdikaris, and G.
Karniadakis, J. Comput. Phys., 378 (2019), pp. 686--707, are effective in solving integer …
Karniadakis, J. Comput. Phys., 378 (2019), pp. 686--707, are effective in solving integer …
An overview of machine learning-based techniques for solving optimization problems in communications and signal processing
Despite the growing interest in the interplay of machine learning and optimization, existing
contributions remain scattered across the research board, and a comprehensive overview …
contributions remain scattered across the research board, and a comprehensive overview …