Management of Resource at the Network Edge for Federated Learning

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.

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

References in corpus (30)

• Federated Learning: Strategies for Improving Communication Efficiency
• Federated Learning with Non-IID Data
• Convergence of Edge Computing and Deep Learning: A Comprehensive Survey
• A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection
• Federated Learning for Mobile Keyboard Prediction
• In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning
• Towards Federated Learning at Scale: System Design
• Secure Federated Transfer Learning
• Revisiting Distributed Synchronous SGD
• A Survey on Multi-Task Learning
• Distributed Federated Learning for Ultra-Reliable Low-Latency Vehicular Communications
• Applied Federated Learning: Improving Google Keyboard Query Suggestions
• Asynchronous Federated Optimization
• The Internet of Things, Fog and Cloud Continuum: Integration and Challenges
• Personalized Federated Learning: A Meta-Learning Approach
• Fair Resource Allocation in Federated Learning
• Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning
• Federated Meta-Learning with Fast Convergence and Efficient Communication
• Multi-Stage Hybrid Federated Learning over Large-Scale D2D-Enabled Fog Networks
• Edge Intelligence: Architectures, Challenges, and Applications
• Federated Multi-Task Learning
• IBM Federated Learning: an Enterprise Framework White Paper V0.1
• Scheduling in distributed systems: A cloud computing perspective
• Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategies
• Multi-Participant Multi-Class Vertical Federated Learning
• Asymmetrical Vertical Federated Learning
• Edge-as-a-Service: Towards Distributed Cloud Architectures
• To Talk or to Work: Flexible Communication Compression for Energy Efficient Federated Learning over Heterogeneous Mobile Edge Devices
• eDisco: Discovering Edge Nodes Along the Path
• Deploying Federated Learning in Large-Scale Cellular Networks: Spatial Convergence Analysis