Resource Allocation for Federated Learning with Heterogeneous Computing Capability in Cloud–Edge–Client IoT Architecture
A federated learning (FL) framework for cloud–edge–client collaboration performs local aggregation of model parameters through edges, reducing communication overhead from clients to the cloud. This framework is particularly suitable for Internet of Things (IoT)-based secure computing scenarios that...
Saved in:
Main Authors: | Xubo Zhang, Yang Luo |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2025-05-01
|
Series: | Future Internet |
Subjects: | |
Online Access: | https://www.mdpi.com/1999-5903/17/6/243 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Client Selection in Federated Learning on Resource-Constrained Devices: A Game Theory Approach
by: Zohra Dakhia, et al.
Published: (2025-07-01) -
Optimizing resource allocation in industrial IoT with federated machine learning and edge computing integration
by: Ala'a R. Al-Shamasneh, et al.
Published: (2025-09-01) -
Adaptive Federated Learning With Reinforcement Learning-Based Client Selection for Heterogeneous Environments
by: Shamim Ahmed, et al.
Published: (2025-01-01) -
Federated Learning for All: A Reinforcement Learning-Based Approach for Ensuring Fairness in Client Selection
by: Saeedeh Ghazi, et al.
Published: (2025-01-01) -
An Intelligent Client Selection Algorithm of Federated Learning for Class-imbalance
by: ZHU Suxia, et al.
Published: (2024-04-01)