Wireless for Machine Learning
arXiv:2008.13492
Abstract
As data generation increasingly takes place on devices without a wired connection, machine learning (ML) related traffic will be ubiquitous in wireless networks. Many studies have shown that traditional wireless protocols are highly inefficient or unsustainable to support ML, which creates the need for new wireless communication methods. In this survey, we give an exhaustive review of the state-of-the-art wireless methods that are specifically designed to support ML services over distributed datasets. Currently, there are two clear themes within the literature, analog over-the-air computation and digital radio resource management optimized for ML. This survey gives a comprehensive introduction to these methods, reviews the most important works, highlights open problems, and discusses application scenarios.
References in corpus (35)
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- Distilling the Knowledge in a Neural Network
- Natural Language Processing (almost) from Scratch
- Communication-Efficient Learning of Deep Networks from Decentralized Data
- Federated Learning: Strategies for Improving Communication Efficiency
- Federated Learning with Non-IID Data
- Federated Optimization: Distributed Machine Learning for On-Device Intelligence
- Convergence of Edge Computing and Deep Learning: A Comprehensive Survey
- Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence
- Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training
- Speeding Up Distributed Machine Learning Using Codes
- A Survey of Model Compression and Acceleration for Deep Neural Networks
- How To Backdoor Federated Learning
- To prune, or not to prune: exploring the efficacy of pruning for model compression
- Machine Learning at the Wireless Edge: Distributed Stochastic Gradient Descent Over-the-Air
- Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data
- Mitigating Sybils in Federated Learning Poisoning
- Fair Resource Allocation in Federated Learning
- Agnostic Federated Learning
- On Analog Gradient Descent Learning over Multiple Access Fading Channels
- Federated Learning in Mobile Edge Networks: A Comprehensive Survey
- Communication-Efficient Distributed Dual Coordinate Ascent
- A Joint Learning and Communications Framework for Federated Learning over Wireless Networks
- Active Federated Learning
- Over-the-air Function Computation in Sensor Networks
- Towards an Intelligent Edge: Wireless Communication Meets Machine Learning
- Federated Learning via Over-the-Air Computation
- Energy-Efficient Radio Resource Allocation for Federated Edge Learning
- Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)
- Communication-Efficient Edge AI: Algorithms and Systems
- Wireless Federated Learning with Local Differential Privacy
- Over-the-Air Computation via Intelligent Reflecting Surfaces
- Energy-Aware Analog Aggregation for Federated Learning with Redundant Data
- An Overview of Data-Importance Aware Radio Resource Management for Edge Machine Learning
- Data-Importance Aware User Scheduling for Communication-Efficient Edge Machine Learning