Bandwidth Allocation for Multiple Federated Learning Services in Wireless Edge Networks

Xu, Jie; Wang, Heqiang; Chen, Lixing

doi:10.48550/arxiv.2101.03627

Cited by 4 publications

(5 citation statements)

References 38 publications

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“…Allocating resources in this multiservice FL scenario is a tough challenge due to sharing of the same spectrum among those services. In a study, 76 it has been highlighted that multiservice FL's efficiency depends upon both intraservice and interservice spectrum allocation. Intraservice refers to the general client-level resource allocation which we had been discussing prior to this.…”

Section: Efficient Resource Managementmentioning

confidence: 99%

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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Section: Efficient Resource Managementmentioning

confidence: 99%

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

Shome

Waqar

Khan

2022

Trans Emerging Tel Tech

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“…Two algorithms are designed, including a centralized algorithm based on the block coordinate descent method and a decentralized algorithm based on the Jacobi-Proximal alternating direction method of multipliers (ADMM) approach. Multiple FL services are also investigated in a recent work [46]. Different from [44] where diffident FL services running on the same devices, [46] considers that each device runs only one FL service and multiple FL services are available at multiple IoMT devices in the network.…”

Section: Resource-aware Flmentioning

confidence: 99%

“…Multiple FL services are also investigated in a recent work [46]. Different from [44] where diffident FL services running on the same devices, [46] considers that each device runs only one FL service and multiple FL services are available at multiple IoMT devices in the network. A bandwidth allocation scheme is designed to allocate bandwidth resources among clients running the same FL service and among FL services.…”

Section: Resource-aware Flmentioning

confidence: 99%

“…A promising solution in this case is designing incentive mechanisms to motivate hospital/health organizations to use high-quality data for training as well as report reliable updates to the aggregation server. Game theory and blockchain are two important tools for designing incentive mechanisms [32,46,55,56,68]. The training requirements (e.g., changes of data types, changes of learning rates, changes of training purpose-classification or regression) should be configured in a flexible manner so that the FL entity such as nurses, doctors, and patients can adjust their actions easily and appropriately.…”

Section: Quality Of Federated Healthcare Training Datamentioning

confidence: 99%

See 1 more Smart Citation

Federated Learning for Smart Healthcare: A Survey

Nguyen¹,

Pham²,

Pathirana³

et al. 2021

Preprint

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Recent advances in communication technologies and Internet-of-Medical-Things have transformed smart healthcare enabled by artificial intelligence (AI). Traditionally, AI techniques require centralized data collection and processing that may be infeasible in realistic healthcare scenarios due to the high scalability of modern healthcare networks and growing data privacy concerns. Federated Learning (FL), as an emerging distributed collaborative AI paradigm, is particularly attractive for smart healthcare, by coordinating multiple clients (e.g., hospitals) to perform AI training without sharing raw data. Accordingly, we provide a comprehensive survey on the use of FL in smart healthcare. First, we present the recent advances in FL, the motivations, and the requirements of using FL in smart healthcare. The recent FL designs for smart healthcare are then discussed, ranging from resource-aware FL, secure and privacy-aware FL to incentive FL and personalized FL. Subsequently, we provide a state-of-the-art review on the emerging applications of FL in key healthcare domains, including health data management, remote health monitoring, medical imaging, and COVID-19 detection. Several recent FL-based smart healthcare projects are analyzed, and the key lessons learned from the survey are also highlighted. Finally, we discuss interesting research challenges and possible directions for future FL research in smart healthcare.

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“…Allocating resources in this multi-service FL scenario is a tough challenge due to sharing of the same spectrum among those services. In a study [76], it has been highlighted that multi-service FL's efficiency depends upon both intra-service and inter-service spectrum allocation. Intra-service refers to the general clientlevel resource allocation which we had been discussing prior to this.…”

Section: Efficient Resource Managementmentioning

confidence: 99%

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

Shome¹,

Waqar²,

Khan³

2021

Preprint

View full text Add to dashboard Cite

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 e.g., cloud server, that significantly increases the communication overheads and also raises severe privacy concerns. Towards 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 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 paper is to bridge this gap in literature by providing a timely and comprehensive discussion on the interdependency between FL and wireless communications.

show abstract

Bandwidth Allocation for Multiple Federated Learning Services in Wireless Edge Networks

Cited by 4 publications

References 38 publications

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

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

Federated Learning for Smart Healthcare: A Survey

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

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