Management of digital twin-driven IoT using federated learning
Internet of Things (IoT), Digital Twin (DT), and Federated Learning (FL) are redefining the
future vision of globalization. While IoT is about sensing data from physical devices, DTs …
future vision of globalization. While IoT is about sensing data from physical devices, DTs …
Multi-UAV-assisted federated learning for energy-aware distributed edge training
Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) has largely
extended the border and capacity of artificial intelligence of things (AIoT) by providing a key …
extended the border and capacity of artificial intelligence of things (AIoT) by providing a key …
CRAS-FL: Clustered resource-aware scheme for federated learning in vehicular networks
As a promising distributed learning paradigm, Federated Learning (FL) is expected to meet
the ever-increasing needs of Machine Learning (ML) based applications in Intelligent …
the ever-increasing needs of Machine Learning (ML) based applications in Intelligent …
Toward securing federated learning against poisoning attacks in zero touch B5G networks
The zero Touch Management (ZSM) concept in 5G and Beyond networks (B5G) aims to
automate the management and orchestration of running network slices. This requires heavy …
automate the management and orchestration of running network slices. This requires heavy …
Communication-efficient personalized federated meta-learning in edge networks
Due to the privacy breach risks and data aggregation of traditional centralized machine
learning (ML) approaches, applications, data and computing power are being pushed from …
learning (ML) approaches, applications, data and computing power are being pushed from …
Rl-based federated learning framework over blockchain (rl-fl-bc)
Federated learning (FL) paradigms aim to amalgamate diverse data properties stored locally
at each user, while preserving data privacy through sharing users' learning experiences and …
at each user, while preserving data privacy through sharing users' learning experiences and …
Toward heterogeneous environment: Lyapunov-orientated imphetero reinforcement learning for task offloading
F Sun, Z Zhang, X Chang, K Zhu - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Task offloading combined with reinforcement learning (RL) is a promising research direction
in edge computing. However, the intractability in the training of RL and the heterogeneity of …
in edge computing. However, the intractability in the training of RL and the heterogeneity of …
Federated learning on the go: Building stable clusters and optimizing resources on the road
With the proliferation of Internet of Things, leveraging federated learning (FL) for
collaborative model training has become paramount. It has turned into a powerful tool to …
collaborative model training has become paramount. It has turned into a powerful tool to …
A dynamic receptive field and improved feature fusion approach for federated learning in financial credit risk assessment
R Li, Y Cao, Y Shu, J Guo, B Shi, J Yu, Y Di, Q Zuo… - Scientific Reports, 2024 - nature.com
Federated Learning (FL) uses local data to perform distributed training on clients and
combines resulting models on a public server to mitigate privacy exposure by avoiding data …
combines resulting models on a public server to mitigate privacy exposure by avoiding data …
A Practical Federated Learning Framework With Truthful Incentive in UAV-Assisted Crowdsensing
The integration of unmanned aerial vehicles (UAVs) and artificial intelligence (AI) has
garnered significant interest as a promising paradigm for facilitating intelligent and pervasive …
garnered significant interest as a promising paradigm for facilitating intelligent and pervasive …