Distributed Learning in Wireless Networks: Recent Progress and Future Challenges
| Autor: | Deniz Gunduz, Walid Saad, Kaibin Huang, Mingzhe Chen, Mehdi Bennis, Aneta Vulgarakis Feljan, H. Vincent Poor |
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| Přispěvatelé: | Commission of the European Communities, Engineering & Physical Science Research Council (EPSRC) |
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Technology Computer Science - Machine Learning Information privacy Computer science Distributed computing 02 engineering and technology ALLOCATION Machine Learning (cs.LG) Data modeling Engineering DESIGN COMMUNICATION-EFFICIENT 0202 electrical engineering electronic engineering information engineering federated distillation Reinforcement learning Wireless networks wireless edge networks Measurement federated learning Wireless network POWER-CONTROL Data models 0906 Electrical and Electronic Engineering Telecommunications Performance evaluation Enhanced Data Rates for GSM Evolution Networking & Telecommunications STOCHASTIC GRADIENT DESCENT Edge device multi-agent reinforcement learning Computer Networks and Communications Computer Science - Information Theory 0805 Distributed Computing UNCODED TRANSMISSION THE-AIR COMPUTATION 1005 Communications Technologies Training Overhead (computing) Wireless Distance learning Electrical and Electronic Engineering 6G Distributed learning Science & Technology business.industry Information Theory (cs.IT) distributed inference Engineering Electrical & Electronic 020206 networking & telecommunications Computer aided instruction business |
| Zdroj: | IEEE Journal on Selected Areas in Communications. 39:3579-3605 |
| ISSN: | 1558-0008 0733-8716 |
| DOI: | 10.1109/jsac.2021.3118346 |
| Popis: | The next-generation of wireless networks will enable many machine learning (ML) tools and applications to efficiently analyze various types of data collected by edge devices for inference, autonomy, and decision making purposes. However, due to resource constraints, delay limitations, and privacy challenges, edge devices cannot offload their entire collected datasets to a cloud server for centrally training their ML models or inference purposes. To overcome these challenges, distributed learning and inference techniques have been proposed as a means to enable edge devices to collaboratively train ML models without raw data exchanges, thus reducing the communication overhead and latency as well as improving data privacy. However, deploying distributed learning over wireless networks faces several challenges including the uncertain wireless environment (e.g., dynamic channel and interference), limited wireless resources (e.g., transmit power and radio spectrum), and hardware resources (e.g., computational power). This paper provides a comprehensive study of how distributed learning can be efficiently and effectively deployed over wireless edge networks. We present a detailed overview of several emerging distributed learning paradigms, including federated learning, federated distillation, distributed inference, and multi-agent reinforcement learning. For each learning framework, we first introduce the motivation for deploying it over wireless networks. Then, we present a detailed literature review on the use of communication techniques for its efficient deployment. We then introduce an illustrative example to show how to optimize wireless networks to improve its performance. Finally, we introduce future research opportunities. In a nutshell, this paper provides a holistic set of guidelines on how to deploy a broad range of distributed learning frameworks over real-world wireless communication networks. |
| Databáze: | OpenAIRE |
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