Bandwidth Allocation for Multiple Federated Learning Services in Wireless Edge Networks

Xu, Jie; Wang, Heqiang; Chen, Lixing

doi:10.1109/twc.2021.3113346

Cited by 34 publications

(18 citation statements)

References 30 publications

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“…[174] Joint client selection and bandwidth allocation considering the later-is-better phenomenon of FL. [175] Joint time slot and bandwidth allocation with multiple co-existing FL services that share wireless resources.…”

Section: Ref Summarymentioning

confidence: 99%

“…Differently from [118], [174], the authors in [175] consider a scenario with multiple FL services co-existing and sharing resources in a wireless network and propose bandwidth allocation to ensure sufficient client participation for each FL service. Specifically, they propose a two-level resource management framework comprising of intra-and inter-service resource allocation.…”

Section: Participation Maximizationmentioning

confidence: 99%

See 1 more Smart Citation

Wireless for Machine Learning: A Survey

Hellström¹,

Silva²,

Amiri³

et al. 2022

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As data generation increasingly takes place on devices without a wired connection, Machine Learning (ML) related traffic will be ubiquitous in wireless networks. Many studies have shown that traditional wireless protocols are highly inefficient or unsustainable to support ML, which creates the need for new wireless communication methods. In this monograph, we give a comprehensive review of the state-of-the-art wireless methods that are specifically designed to support ML services over distributed datasets. Currently, there are two clear themes within the literature, analog over-the-air computation and digital radio resource management optimized for ML. This survey gives an introduction to these methods, reviews the most important works, highlights open problems, and discusses application scenarios.

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Section: Ref Summarymentioning

confidence: 99%

Section: Participation Maximizationmentioning

confidence: 99%

Wireless for Machine Learning: A Survey

Hellström¹,

Silva²,

Amiri³

et al. 2022

View full text Add to dashboard Cite

show abstract

“…There are many algorithms that have been proposed to obtain the approximate solutions for the problem, such as the Lagrangian relaxation approach [66]. In FL, the combinatorial auction is used to allocate network operator's bandwidth to multiple FL SP as proposed in [67].…”

Section: B Auctionmentioning

confidence: 99%

“…However, as the diversification of ML-driven applications develop and grow, it is predicted that the wireless network will host multiple co-existing FL services. To fill the gap, the authors in [67] considered a multi-FL service scenario, where FL service providers (SPs) cooperate with each other to maximize the overall system performance or are selfish to maximize their own performance. For the cooperative FL SPs, intra-service and inter-service resource allocations are considered.…”

Section: A Incentive Mechanisms Based On Sealed-bid Auctionsmentioning

confidence: 99%

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Incentive Mechanisms for Federated Learning: From Economic and Game Theoretic Perspective

Tu¹,

Luong²,

Niyato³

et al. 2021

Preprint

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Federated learning (FL) becomes popular and has shown great potentials in training large-scale machine learning (ML) models without exposing the owners' raw data. In FL, the data owners can train ML models based on their local data and only send the model updates rather than raw data to the model owner for aggregation. To improve learning performance in terms of model accuracy and training completion time, it is essential to recruit sufficient participants. Meanwhile, the data owners are rational and may be unwilling to participate in the collaborative learning process due to the resource consumption. To address the issues, there have been various works recently proposed to motivate the data owners to contribute their resources. In this paper, we provide a comprehensive review for the economic and game theoretic approaches proposed in the literature to design various schemes for incentivizing data owners to participate in FL training process. In particular, we first present the fundamentals and background of FL, economic theories commonly used in incentive mechanism design. Then, we review applications of game theory and economic approaches applied for incentive mechanisms design of FL. Finally, we highlight some open issues and future research directions concerning incentive mechanism design of FL.

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Federated learning and next generation wireless communications: A survey on bidirectional relationship

Shome

Waqar

Khan

2022

Trans Emerging Tel Tech

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In order to meet the extremely heterogeneous requirements of the next generation wireless communication networks, research community is increasingly dependent on using machine‐learning solutions for real‐time decision‐making and radio resource management. Traditional machine learning employs fully centralized architecture in which the entire training data is collected at one node for example, cloud server, that significantly increases the communication overheads and also raises severe privacy concerns. Toward this end, a distributed machine‐learning paradigm termed as federated learning (FL) has been proposed recently. In FL, each participating edge device trains its local model by using its own training data. Then, via the wireless channels the weights or parameters of the locally trained models are sent to the central parameter server (PS), that aggregates them and updates the global model. On one hand, FL plays an important role for optimizing the resources of wireless communication networks, on the other hand, wireless communications is crucial for FL. Thus, a “bidirectional” relationship exists between FL and wireless communications. Although FL is an emerging concept, many publications have already been published in the domain of FL and its applications for next generation wireless networks. Nevertheless, we noticed that none of the works have highlighted the bidirectional relationship between FL and wireless communications. Therefore, the purpose of this survey article is to bridge this gap in literature by providing a timely and comprehensive discussion on the interdependency between FL and wireless communications.

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Bandwidth Allocation for Multiple Federated Learning Services in Wireless Edge Networks

Cited by 34 publications

References 30 publications

Wireless for Machine Learning: A Survey

Wireless for Machine Learning: A Survey

Incentive Mechanisms for Federated Learning: From Economic and Game Theoretic Perspective

Federated learning and next generation wireless communications: A survey on bidirectional relationship

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