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
-
Balanced coarse-to-fine federated learning for noisy heterogeneous clients
by: Longfei Han, et al.
Published: (2025-01-01) -
MAB-Based Online Client Scheduling for Decentralized Federated Learning in the IoT
by: Zhenning Chen, et al.
Published: (2025-04-01) -
Mobility Prediction and Resource-Aware Client Selection for Federated Learning in IoT
by: Rana Albelaihi
Published: (2025-03-01) -
Personalized client-edge-cloud hierarchical federated learning in mobile edge computing
by: Chunmei Ma, et al.
Published: (2024-12-01) -
DFL: Dynamic Federated Split Learning in Heterogeneous IoT
by: Eric Samikwa, et al.
Published: (2024-01-01)