Federated Learning under Channel Uncertainty: Joint Client Scheduling and Resource Allocation

Wadu, Madhusanka Manimel; Samarakoon, Sumudu; Bennis, Mehdi

doi:10.1109/wcnc45663.2020.9120649

Cited by 56 publications

(37 citation statements)

References 7 publications

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“…Being able to find a compromise with this trade-off is still a challenge when attempting to solve the privacy issue [112], [116]. The authors of [117] also note that the biggest issues in FL are mainly security and privacy. As such, efficient FL algorithms that deliver models with high performance and privacy protection without adding computational burden are desirable [118], [119].…”

Section: Challenges and Limitations Of Federated Learningmentioning

confidence: 99%

Federated Learning: A Survey on Enabling Technologies, Protocols, and Applications

et al. 2020

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This paper provides a comprehensive study of Federated Learning (FL) with an emphasis on enabling software and hardware platforms, protocols, real-life applications and use-cases. FL can be applicable to multiple domains but applying it to different industries has its own set of obstacles. FL is known as collaborative learning, where algorithm(s) get trained across multiple devices or servers with decentralized data samples without having to exchange the actual data. This approach is radically different from other more established techniques such as getting the data samples uploaded to servers or having data in some form of distributed infrastructure. FL on the other hand generates more robust models without sharing data, leading to privacy-preserved solutions with higher security and access privileges to data. This paper starts by providing an overview of FL. Then, it gives an overview of technical details that pertain to FL enabling technologies, protocols, and applications. Compared to other survey papers in the field, our objective is to provide a more thorough summary of the most relevant protocols, platforms, and real-life use-cases of FL to enable data scientists to build better privacy-preserving solutions for industries in critical need of FL. We also provide an overview of key challenges presented in the recent literature and provide a summary of related research work. Moreover, we explore both the challenges and advantages of FL and present detailed service use-cases to illustrate how different architectures and protocols that use FL can fit together to deliver desired results.

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Section: Challenges and Limitations Of Federated Learningmentioning

confidence: 99%

Federated Learning: A Survey on Enabling Technologies, Protocols, and Applications

et al. 2020

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“…The poor channel conditions in both uplink and downlink introduces stragglers from the channel sampling and pilot transmissions therein require high reliable (possibly dedicated) resources as well as introduce significant latency to the training process. To overcome the cons of channel measurement, GPR-based channel estimation can be adopted in FL (GPR-FL) [130]. By modeling the dynamic channel states as stochastic processes with a Gaussian prior, time series prediction in GPR can be used to estimate the channels and their uncertainty (Sec.…”

Section: Gpr Aided Flmentioning

confidence: 99%

Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applications

et al. 2021

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Machine learning (ML) is a promising enabler for the fifth generation (5G) communication systems and beyond. By imbuing intelligence into the network edge, edge nodes can proactively carry out decision-making, and thereby react to local environmental changes and disturbances while experiencing zero communication latency. To achieve this goal, it is essential to cater for high ML inference accuracy at scale under time-varying channel and network dynamics, by continuously exchanging fresh data and ML model updates in a distributed way. Taming this new kind of data traffic boils down to improving the communication efficiency of distributed learning by optimizing communication payload types, transmission techniques, and scheduling, as well as ML architectures, algorithms, and data processing methods. To this end, this article aims to provide a holistic overview of relevant communication and ML principles, and thereby present communication-efficient and distributed learning frameworks with selected use cases. SIGNIFICANCE AND MOTIVATIONThe pursuit of extremely stringent latency and reliability guarantees is essential in the fifth generation (5G) communication system and beyond [1], [2]. In a wirelessly automated factory, the remote control of assembly robots should provision the same level of target latency and reliability offered by existing wired factory systems. To this end, for instance, control packets should be delivered within 1 ms with 99.99999% reliability [3]- [5]. Things are becoming even more challenging in the emerging mission-critical applications beyond 5G. A prime example is the forthcoming nonterrestrial networks consisting of a massive constellation of low-altitude earth orbit (LEO) satellites [6]- [11]. Given such

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“…In addition, although FL is tolerant toward the dropping out of participating clients, the inclusion of more clients has advantages in terms of training accuracy, owing to the fact that such an inclusion covers larger training datasets. To optimize resource allocation, the authors of [21,22] proposed a client-scheduling algorithm for communication-efficient FL; however, they did not consider the energy consumption problems of power-hungry devices.…”

Section: Motivationmentioning

confidence: 99%

Optimal User Selection for High-Performance and Stabilized Energy-Efficient Federated Learning Platforms

et al. 2020

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Federated learning-enabled edge devices train global models by sharing them while avoiding local data sharing. In federated learning, the sharing of models through communication between several clients and central servers results in various problems such as a high latency and network congestion. Moreover, battery consumption problems caused by local training procedures may impact power-hungry clients. To tackle these issues, federated edge learning (FEEL) applies the network edge technologies of mobile edge computing. In this paper, we propose a novel control algorithm for high-performance and stabilized queue in FEEL system. We consider that the FEEL environment includes the clients transmit data to associated federated edges; these edges then locally update the global model, which is downloaded from the central server via a backhaul. Obtaining greater quantities of local data from the clients facilitates more accurate global model construction; however, this may be harmful in terms of queue stability in the edge, owing to substantial data arrivals from the clients. Therefore, the proposed algorithm varies the number of clients selected for transmission, with the aim of maximizing the time-averaged federated learning accuracy subject to queue stability. Based on this number of clients, the federated edge selects the clients to transmit on the basis of resource status.

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Federated Learning under Channel Uncertainty: Joint Client Scheduling and Resource Allocation

Cited by 56 publications

References 7 publications

Federated Learning: A Survey on Enabling Technologies, Protocols, and Applications

Federated Learning: A Survey on Enabling Technologies, Protocols, and Applications

Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applications

Optimal User Selection for High-Performance and Stabilized Energy-Efficient Federated Learning Platforms

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