Edge Cloud Computing and Federated–Split Learning in Internet of Things-Reference-Cited by-同舟云学术

Edge Cloud Computing and Federated–Split Learning in Internet of Things

Published:2024-06-28 Issue:7 Volume:16 Page:227
ISSN:1999-5903
Container-title:Future Internet
language:en
Short-container-title:Future Internet

Author:

Duan Qiang¹^ORCID,Lu Zhihui²^ORCID

Affiliation:

1. Information Sciences and Technology Department, Pennsylvania State University, Abington, PA 19001, USA

2. School of Computer Science, Fudan University, Shanghai 200437, China

Abstract

The wide deployment of the Internet of Things (IoT) necessitates new machine learning (ML) methods and distributed computing paradigms to enable various ML-based IoT applications to effectively process huge amounts of data [...]

Publisher

MDPI AG

Link

https://www.mdpi.com/1999-5903/16/7/227/pdf

Reference12 articles.

1. Nikolaidis, F., Symeonides, M., and Trihinas, D. (2023). Towards Efficient Resource Allocation for Federated Learning in Virtualized Managed Environments. Future Internet, 15.

2. Cai, W., and Duan, F. (2023). Task Scheduling for Federated Learning in Edge Cloud Computing Environments by Using Adaptive-Greedy Dingo Optimization Algorithm and Binary Salp Swarm Algorithm. Future Internet, 15.

3. Ridolfi, L., Naseh, D., Shinde, S.S., and Tarchi, D. (2023). Implementation and Evaluation of a Federated Learning Framework on Raspberry PI Platforms for IoT 6G Applications. Future Internet, 15.

4. Al-Saedi, A.A., Boeva, V., and Casalicchio, E. (2022). FedCO: Communication-efficient federated learning via clustering optimization. Future Internet, 14.

5. Yu, L., Sun, X., Albelaihi, R., and Yi, C. (2023). Latency-Aware Semi-Synchronous Client Selection and Model Aggregation for Wireless Federated Learning. Future Internet, 15.