Resource optimizing federated learning for use with IoT: A systematic review-Reference-Cited by-同舟云学术

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Resource optimizing federated learning for use with IoT: A systematic review

Published:2023-05 Issue: Volume:175 Page:92-108
ISSN:0743-7315
Container-title:Journal of Parallel and Distributed Computing
language:en
Short-container-title:Journal of Parallel and Distributed Computing

Author:

da Silva Leylane Graziele Ferreira^ORCID,Sadok Djamel F.H.,Endo Patricia Takako

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Networks and Communications,Hardware and Architecture,Theoretical Computer Science,Software

Reference79 articles.

1. Joint task and resource allocation for mobile edge learning;Abutuleb,2020

2. Reducing communication overhead of federated learning through clustering analysis;Al-Saedi,2021

3. Federated learning algorithms to optimize the client and cost selections;Alferaidi;Math. Probl. Eng.,2022

4. Energy-efficient device assignment and task allocation in multi-orchestrator mobile edge learning;Allahham,2021

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1. Small models, big impact: A review on the power of lightweight Federated Learning;Future Generation Computer Systems;2025-01

2. Quantum-empowered federated learning and 6G wireless networks for IoT security: Concept, challenges and future directions;Future Generation Computer Systems;2024-11

3. Elastic Federated Learning with Kubernetes Vertical Pod Autoscaler for edge computing;Future Generation Computer Systems;2024-09

4. Efficient knowledge management for heterogeneous federated continual learning on resource-constrained edge devices;Future Generation Computer Systems;2024-07

5. Intersection of machine learning and mobile crowdsourcing: a systematic topic-driven review;Personal and Ubiquitous Computing;2024-06-10

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