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 …
A Docker-based federated learning framework design and deployment for multi-modal data stream classification
In the high-performance computing (HPC) domain, federated learning has gained immense
popularity. Especially in emotional and physical health analytics and experimental facilities …
popularity. Especially in emotional and physical health analytics and experimental facilities …
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 …
Communication efficient federated learning with data offloading in fog-based IoT environment
Federated Learning (FL) has become a popular distributed machine learning technique that
preserves privacy of data set generated by the Internet of Things (IoT) devices. However …
preserves privacy of data set generated by the Internet of Things (IoT) devices. However …
Trust driven On-Demand scheme for client deployment in Federated Learning
Containerization technology plays a crucial role in Federated Learning (FL) setups,
expanding the pool of potential clients and ensuring the availability of specific subsets for …
expanding the pool of potential clients and ensuring the availability of specific subsets for …
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 …
[HTML][HTML] Devising an actor-based middleware support to federated learning experiments and systems
Federated Learning (FL) recently emerged as a practical privacy-preserving paradigm to
exploit data distributed over separated repositories for Machine Learning purposes, with no …
exploit data distributed over separated repositories for Machine Learning purposes, with no …
Towards cluster-based split federated learning approach for continuous user authentication
In today's rapidly evolving technological landscape, ensuring the security of systems
requires continuous authentication over sessions and comprehensive access management …
requires continuous authentication over sessions and comprehensive access management …
WFSL: Warmup-Based Federated Sequential Learning
Federated learning gained importance in sensitive IoT environments by creating a privacy-
preserving ecosystem where participants share machine-learning models instead of raw …
preserving ecosystem where participants share machine-learning models instead of raw …