Management of Resource at the Network Edge for Federated Learning

2021/07/07 by Silvana Trindade, Trindade, Silvana, Luiz F. Bittencourt +3 · 1 citation
Computer Science · #Age of Information Optimization #FOS: Computer and information sciences #IoT and Edge/Fog Computing #Machine Learning (cs.LG) #Networking and Internet Architecture (cs.NI) #Privacy-Preserving Technologies in Data #cs.LG #cs.NI

paper · pdf · doi:10.48550/arxiv.2107.03428

arXiv admin note: text overlap with arXiv:1803.05255 by other authors

openalex publication_date 2021/07/07 · arxiv created 2022/02/04 · arxiv updated 2022/02/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Federated learning has been explored as a promising solution for training at the edge, where end devices collaborate to train models without sharing data with other entities. Since the execution of these learning models occurs at the edge, where resources are limited, new solutions must be developed. In this paper, we describe the recent work on resource management at the edge, and explore the challenges and future directions to allow the execution of federated learning at the edge. Some of the problems of this management, such as discovery of resources, deployment, load balancing, migration, and energy efficiency will be discussed in the paper.

Citations

• Federated Learning: Strategies for Improving Communication Efficiency
• Federated Multi-Task Learning
• A Survey on Multi-Task Learning
• Edge-as-a-Service: Towards Distributed Cloud Architectures
• Applied Federated Learning: Improving Google Keyboard Query Suggestions
• Towards Federated Learning at Scale: System Design
• Fair Resource Allocation in Federated Learning
• Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing
• Federated Meta-Learning with Fast Convergence and Efficient Communication
• Advances and Open Problems in Federated Learning
• Multi-Participant Multi-Class Vertical Federated Learning
• Personalized Federated Learning: A Meta-Learning Approach
• Federated Learning in Mobile Edge Networks: A Comprehensive Survey
• Asymmetrical Vertical Federated Learning
• Edge Intelligence: Architectures, Challenges, and Applications
• IBM Federated Learning: an Enterprise Framework White Paper V0.1
• To Talk or to Work: Flexible Communication Compression for Energy Efficient Federated Learning over Heterogeneous Mobile Edge Devices
• Deploying Federated Learning in Large-Scale Cellular Networks: Spatial Convergence Analysis
• Addressing the Challenges in Federating Edge Resources

Cited by

• Quantum Federated Learning: A Comprehensive Survey

Related

• Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanism
• Federated Learning Framework in Fogbus2-based Edge Computing Environments
• A Comprehensive Survey on Joint Resource Allocation Strategies in Federated Edge Learning
• Toward Multiple Federated Learning Services Resource Sharing in Mobile Edge Networks
• Federated Edge Learning : Design Issues and Challenges