On In-network learning. A Comparative Study with Federated and Split Learning
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Abstract (arXiv)
In this paper, we consider a problem in which distributively extracted features are used for performing inference in wireless networks. We elaborate on our proposed architecture, which we herein refer to as "in-network learning", provide a suitable loss function and discuss its optimization using neural networks. We compare its performance with both Federated- and Split learning; and show that this architecture offers both better accuracy and bandwidth savings.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 12 of its 18 references that are in the map. First posted on arXiv 30 Apr 2021.
Builds on
12 of 18 references.
References · 12
- 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
- 2013Auto-Encoding Variational Bayescited by 3,980
- 2019Object Detection in 20 Years: A Surveycited by 528
- 2018Distributed learning of deep neural network over multiple agentscited by 277
- 2015A Neural Conversational Modelcited by 195
- 2019Federated Learning over Wireless Fading Channelscited by 178
- 2019Scheduling Policies for Federated Learning in Wireless Networkscited by 131
- 2016CSI-based Fingerprinting for Indoor Localization: A Deep Learning Approachcited by 39
- 2020Compressing Images by Encoding Their Latent Representations with Relative Entropy Codingcited by 36
- 2018Distributed Variational Representation Learningcited by 32
- 2020On the Information Bottleneck Problems: Models, Connections, Applications and Information Theoretic Viewscited by 28
- 2018The Roles of Supervised Machine Learning in Systems Neurosciencecited by 7
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Citing papers per year: 2021: 2.
2 papers.