A Survey on Over-the-Air Computation

Şahin, Alphan; Yang, Rui

doi:10.1109/comst.2023.3264649

Cited by 31 publications

(3 citation statements)

References 189 publications

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“…AirComp [94] has been recently developed as a new airinterface solution, which merges the concurrent data transmission from multiple devices and performs ''over-the-air'' data aggregation, by exploiting the inherent waveform superposition property of a multi-access channel. the aggregated version of analog modulated local models/gradients simultaneously transmitted by devices.…”

Section: Aircompmentioning

confidence: 99%

Network for Distributed Intelligence: a Survey and Future Perspectives

Campolo

Molinaro

2023

IEEE Access

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To keep pace with the explosive growth of Artificial Intelligence (AI) and Machine Learning (ML)-dominated applications, distributed intelligence solutions are gaining momentum, which exploit cloud facilities, edge nodes and end-devices to increase the overall computational power, meet application requirements, and optimize performance. Despite the benefits in terms of data privacy and efficient usage of resources, distributing intelligence throughout the cloud-to-things continuum raises unprecedented challenges to the network design. Distributed AI/ML components need high-bandwidth, low-latency connectivity to execute learning and inference tasks, while ensuring high-accuracy and energy-efficiency. This paper aims to explore the new challenging distributed intelligence scenario by extensively and critically scanning the main research achievements in the literature. In addition, starting from them, the main building blocks of a network ecosystem that can enable distributed intelligence are identified and the authors' views are dissected to provide guidelines for the design of a ''future network for distributed Intelligence''.

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Section: Aircompmentioning

confidence: 99%

Network for Distributed Intelligence: a Survey and Future Perspectives

Campolo

Molinaro

2023

IEEE Access

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“…Hence, their signals are superposed at the CIS. If there exists synchronization errors, various approaches have been discussed in [20], [63] to acquire an estimation of the superposed signal at the CIS.…”

Section: Transmissionmentioning

confidence: 99%

“…In a recent line of work [16]- [19], it has been shown that, in distributed training tasks, superposition property of the multiple access channel (MAC) can be exploited to use communication resources much more efficiently and to significantly improve the learning and transmission performance. Instead of conventional digital communication, in over-the-air computation (OAC), clients transmit their updates simultaneously in an uncoded manner such that the receiver automatically gets the aggregated signal via the MAC (please refer to [20] for a comprehensive overview of OAC techniques and applications). Hence, besides communication efficiency, OAC also helps preserve the privacy of the clients since individual signals transmitted by the clients are not observed by the receiver [21].…”

Section: Introductionmentioning

confidence: 99%

Over-the-Air Ensemble Inference with Model Privacy

Yılmaz

Hasırcıoglu

Gündüz

2022

2022 IEEE International Symposium on Information Theory (ISIT)

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We consider collaborative inference at the wireless edge, where each client's model is trained independently on their local datasets. Clients are queried in parallel to make an accurate decision collaboratively. In addition to maximizing the inference accuracy, we also want to ensure the privacy of local models. To this end, we leverage the superposition property of the multiple access channel to implement bandwidth-efficient multi-user inference methods. Specifically, we propose different methods for ensemble and multi-view classification that exploit over-the-air computation. We show that these schemes perform better than their orthogonal counterparts with statistically significant differences while using fewer resources and providing privacy guarantees. We also provide experimental results verifying the benefits of the proposed over-the-air multi-user inference approach and perform an ablation study to demonstrate the effectiveness of our design choices. We share the source code of the framework publicly on Github to facilitate further research and reproducibility.

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A Survey on Wireless Data Aggregation Through Over-the-Air Computation

Lee,

Jung

2024

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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A Survey on Over-the-Air Computation

Cited by 31 publications

References 189 publications

Network for Distributed Intelligence: a Survey and Future Perspectives

Network for Distributed Intelligence: a Survey and Future Perspectives

Over-the-Air Ensemble Inference with Model Privacy

A Survey on Wireless Data Aggregation Through Over-the-Air Computation

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