Non-iid data in federated learning: A systematic review with taxonomy, metrics, methods, frameworks and future directions
Recent advances in machine learning have highlighted Federated Learning (FL) as a
promising approach that enables multiple distributed users (so-called clients) to collectively …
promising approach that enables multiple distributed users (so-called clients) to collectively …
An improved big data analytics architecture using federated learning for IoT-enabled urban intelligent transportation systems
The exponential growth of the Internet of Things has precipitated a revolution in Intelligent
Transportation Systems, notably in urban environments. An ITS leverages advancements in …
Transportation Systems, notably in urban environments. An ITS leverages advancements in …
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 …
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 …
A novel privacy-preserving graph convolutional network via secure matrix multiplication
Graph convolutional network (GCN) is one of the most representative methods in the realm
of graph neural networks (GNNs). In the convolution process, GCN combines the structural …
of graph neural networks (GNNs). In the convolution process, GCN combines the structural …
Corrfl: correlation-based neural network architecture for unavailability concerns in a heterogeneous iot environment
The Federated Learning (FL) paradigm faces several challenges that limit its application in
real-world environments. These challenges include the local models' architecture …
real-world environments. These challenges include the local models' architecture …
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 …
FedSW: Federated learning with adaptive sample weights
X Zhao, D Shen - Information Sciences, 2024 - Elsevier
Federated Learning (FL) is a machine learning approach in which a cluster of clients
collaboratively trains a model without sharing the data of any clients. As the datasets of each …
collaboratively trains a model without sharing the data of any clients. As the datasets of each …
Privacy-preserving and communication-efficient stochastic alternating direction method of multipliers for federated learning
Y Zhang, Y Lu, F Liu, C Li, Z Gong, Z Hu, Q Xu - Information Sciences, 2025 - Elsevier
Federated learning constitutes a paradigm in distributed machine learning, wherein model
training unfolds through the exchange of intermediary results between a central server and …
training unfolds through the exchange of intermediary results between a central server and …
Efficient privacy-preserving ML for IoT: Cluster-based split federated learning scheme for non-IID data
In this paper, we propose a solution to address the challenges of varying client resource
capabilities in the IoT environment when using the SplitFed architecture for training models …
capabilities in the IoT environment when using the SplitFed architecture for training models …