Secure Federated Learning in 5G Mobile Networks

Isaksson, Martin; Norrman, Karl

doi:10.48550/arxiv.2004.06700

Cited by 1 publication

(1 citation statement)

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“…Their research has indicated that the usage of a simple neural network results in a significant reduction in network utilization, due to the sharp drop in the amount of data that needs to be shared. Besides being improved by 5G techniques, FL has also been integrated in the 5G Network Data Analytics (NWDA) architecture where it has been used to deal with 5G problems such as Network Data Analytics Function (NWDAF) [73], in order to improve privacy.…”

Section: F Industry Interestmentioning

confidence: 99%

Distributed Learning in Wireless Networks: Recent Progress and Future Challenges

Chen¹,

Gündüz²,

Huang³

et al. 2021

Preprint

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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.

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Section: F Industry Interestmentioning

confidence: 99%