In-network Learning for Distributed Training and Inference in Networks

Moldoveanu, Matei; Zaidi, Abdellatif

doi:10.1109/gcwkshps52748.2021.9682062

Cited by 10 publications

(11 citation statements)

References 16 publications

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“…The whole project is implemented on a tensorflow with an Intel R Core(TM) i7-7700 CPU @2.8 GHz and 8.00 GRAM Machine. [25,50], uniform distribution Nodes number of each VNR [3,5], uniform distribution Transmission rate of each virtual link [3,8], uniform distribution Learning rate of actor network 0.00025 Learning rate of critic network 0.0025…”

Section: Evaluation Settingsmentioning

confidence: 99%

“…However, along with the emergence of Machine Learning (ML) techniques, such as graph neural networks (GNN) [15,16] and deep reinforcement learning (DRL) [17][18][19][20][21], intelligent algorithms are rapidly used to satisfy the diverse requirements of next-generation wireless networks [22] and design communications system [23,24]. ML approaches can not only simulate wireless communication process with inference [25,26] but also assess resource distribution strategy from a deep perspective due to the nature of extracting information [27]. Especially for reinforcement learning algorithms, the parallel framework [16] could speed up the process of embedding and the capability to deal with continuous problems [19] corresponds to the system model of wireless virtual network embedding.…”

Section: Introductionmentioning

confidence: 99%

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Wireless Virtual Network Embedding Algorithm Based on Deep Reinforcement Learning

et al. 2022

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Wireless network virtualization is widely used to solve the ossification problem of networks, such as 5G and the Internet of Things. The most crucial method of wireless network virtualization is virtual network embedding, which allows virtual networks to share the substrate network resources. However, in wireless networks, link interference is an inherent problem while mapping virtual networks because of the characteristics of wireless channels. To distribute resources efficiently and address the problem of interference, a dynamic embedding algorithm with deep reinforcement learning is proposed. During the training stage, we take resource use and interference from substrate networks as observations to train the agent, and then the agent generates a resource allocation strategy. Aiming at realizing load balance, we reshape the reward function considering the execution ratio and residual ratio of substrate network resources as well as the cost consumed by current virtual network request. Numerical tests show that our embedding approach increases the acceptance ratio and maintains better robustness. Moreover, the results also illustrate that our algorithm maintains a high acceptance ratio while producing less interference and lower cost.

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Section: Evaluation Settingsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Wireless Virtual Network Embedding Algorithm Based on Deep Reinforcement Learning

et al. 2022

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“…Finally, we provide a comparative study with (an adaptation of) the FL and the SL algorithms, and experiments that illustrate our results. Part of the results this paper have also been presented in [ 27 , 28 ]. However, in this paper, we go beyond those works by offering a more comprehensive and detailed review of the state-of-the-art.…”

Section: Introductionmentioning

confidence: 99%

In-Network Learning: Distributed Training and Inference in Networks

Moldoveanu

Zaidi

2023

Entropy

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In this paper, we study distributed inference and learning over networks which can be modeled by a directed graph. A subset of the nodes observes different features, which are all relevant/required for the inference task that needs to be performed at some distant end (fusion) node. We develop a learning algorithm and an architecture that can combine the information from the observed distributed features, using the processing units available across the networks. In particular, we employ information-theoretic tools to analyze how inference propagates and fuses across a network. Based on the insights gained from this analysis, we derive a loss function that effectively balances the model’s performance with the amount of information transmitted across the network. We study the design criterion of our proposed architecture and its bandwidth requirements. Furthermore, we discuss implementation aspects using neural networks in typical wireless radio access and provide experiments that illustrate benefits over state-of-the-art techniques.

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“…Moreover, the information bottleneck method has been applied in joint source–channel coding, forwarding and relaying applications [ 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 ] and in distributed sensor networks [ 49 , 50 , 51 , 52 , 53 ] successfully. Related works with a focus on inference with the distributiveness of data among multiple nodes and network learning aspects include [ 54 , 55 , 56 ].…”

Section: Introductionmentioning

confidence: 99%

Information Bottleneck Signal Processing and Learning to Maximize Relevant Information for Communication Receivers

Lewandowsky

Bauch

Stark

2022

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Digital communication receivers extract information about the transmitted data from the received signal in subsequent processing steps, such as synchronization, demodulation and channel decoding. Technically, the receiver-side signal processing for conducting these tasks is complex and hence causes bottleneck situations in terms of power, delay and chip area. Typically, many bits per sample are required to represent and process the received signal in the digital receiver hardware accurately. In addition, demanding arithmetical operations are required in the signal processing algorithms. A popular recent trend is designing entire receiver chains or some of their crucial building blocks from an information theoretical perspective. Signal processing blocks with very simple mathematical operations can be designed to directly maximize the relevant information that flows through them. At the same time, a strong quantization reduces the number of bits processed in the receiver to further lower the complexity. The described system design approach follows the principle of the information bottleneck method. Different authors proposed various ideas to design and implement mutual information-maximizing signal processing units. The first important aim of this article is to explain the fundamental similarities between the information bottleneck method and the functionalities of communication receivers. Based on that, we present and investigate new results on an entire receiver chain that is designed following the information bottleneck design principle. Afterwards, we give an overview of different techniques following the information bottleneck design paradigm from the literature, mainly dealing with channel decoding applications. We analyze the similarities of the different approaches for information bottleneck signal processing. This comparison leads to a general view on information bottleneck signal processing which goes back to the learning of parameters of trainable functions that maximize the relevant mutual information under compression.

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In-network Learning for Distributed Training and Inference in Networks

Cited by 10 publications

References 16 publications

Wireless Virtual Network Embedding Algorithm Based on Deep Reinforcement Learning

Wireless Virtual Network Embedding Algorithm Based on Deep Reinforcement Learning

In-Network Learning: Distributed Training and Inference in Networks

Information Bottleneck Signal Processing and Learning to Maximize Relevant Information for Communication Receivers

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