A Joint Learning and Communications Framework for Federated Learning Over Wireless Networks

Chen, Mingzhe; Yang, Zhaohui; Saad, Walid; Yin, Changchuan; Poor, H. Vincent; Cui, Shuguang

doi:10.1109/twc.2020.3024629

Cited by 1,089 publications

(637 citation statements)

References 24 publications

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“…Since its inception, Federated Learning is evolving, accommodating different application scenarios and having even stronger privacy focus. From Blockchain integration [116] to deployment in wireless networks [117], a large focus is on performance and privacy improvements [118]- [122]. For instance, Niu et al [122] proposed a framework where clients download only portions of the model, train it locally, and then upload the renewed version.…”

Section: E Privacy Technologies For the Cloudmentioning

confidence: 99%

Privacy in the Cloud: A Survey of Existing Solutions and Research Challenges

2021

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Section: E Privacy Technologies For the Cloudmentioning

confidence: 99%

Privacy in the Cloud: A Survey of Existing Solutions and Research Challenges

2021

View full text Add to dashboard Cite

“…Moreover, this feature is expected to be further developed to address use cases such as advanced vehicle-to-vehicle communications, extended coverage via device relaying, industrial applications and virtual reality. Specifically in [6], the authors study distributed learning for D2D communications, whereas in [7], downlink resource allocation and power control are considered for underlay D2D networks. However, there remain several challenges of their own, especially regarding how the devices initiate and maintain data transmission.…”

Section: Introductionmentioning

confidence: 99%

A Distributed Pulse-Based Synchronization Protocol for Half-Duplex D2D Communications

Karatalay

Psaromiligkos

Champagne

et al. 2021

IEEE Open J. Commun. Soc.

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In distributed device-to-device (D2D) communications, no common reference time is available and the devices must employ distributed synchronization techniques. In this context, pulse-based synchronization, which can be implemented by distributed phase-locked loops is preferred due to its scalability. Several factors degrade the performance of pulse-based synchronization, such as duplexing scheme, clock skew and propagation delays. Furthermore, in distributed networks, devices should be aware of the synchronization status of others in order to initiate data communications. To address these prevailing issues, we first introduce a half-duplex timing-advance synchronization algorithm wherein each device alternates between being a transmitter and receiver in their exchange of synchronization pulses at each clock period. Based on this algorithm, we propose a novel fully-distributed pulse-based synchronization protocol for half-duplex D2D communications in 5G wireless networks. The protocol allows participating devices to become aware of the global synchronization status, so that they can complete the synchronization process ideally at the same time and proceed to data communication. In simulation experiments over multi-path frequency selective channels, the proposed synchronization protocol is shown to outperform a benchmark approach from the recent literature over a wide range of conditions, e.g., clock skew, number of devices, and network topology.

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“…DL has been widely investigated over wired/nonheterogeneous computing and communication environments [8], [9], [10], [11]. Recently, researchers have turned their attention to deploying DL models for training on nodes or learners connected via the wireless edge [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23]. DL over the wireless edge, or mobile edge learning (MEL) as we term it, is motivated by two distinct yet practical scenarios: federated learning (FL) and parallelized learning (PL).…”

Section: Introductionmentioning

confidence: 99%

“…Although FL has been extensively studied in literature [12], [13], [14], [15], [19], [20], PL in particular or in general, MEL which comprises both, have been sparsely studied [16], [17], [18]. Some works have proposed algorithms for edge DL without specifically focusing on the resource allocation issues [22], [23].…”

Section: Introductionmentioning

confidence: 99%

“…they are heterogeneity unaware (HU). Although the works of [19], [20] are heterogeneity aware (HA), they ignore the aspect of batch allocation and the PL scenario. The implications of wireless computation/communication heterogeneity on optimizing batch allocation to different learners for maximizing accuracy while satisfying a delay constraint were studied in [16] and dual time and energy constraints in [18].…”

Section: Introductionmentioning

confidence: 99%

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Task Allocation for Mobile Federated and Offloaded Learning with Energy and Delay Constraints

Mohammad

Sorour

Hefeida

2020

2020 IEEE International Conference on Communications Workshops (ICC Workshops)

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This paper extends the paradigm of "mobile edge learning (MEL)" by designing an optimal task allocation scheme for training a machine learning model in an asynchronous manner across mutiple edge nodes or learners connected via a resource-constrained wireless edge network. The optimization is done such that the portion of the task allotted to each learner is completed within a given global delay constraint and a local maximum energy consumption limit. The time and energy consumed are related directly to the heterogeneous communication and computational capabilities of the learners; i.e. the proposed model is heterogeneity aware (HA). Because the resulting optimization is an NP-hard quadratically-constrained integer linear program (QCILP), a two-step suggest-and-improve (SAI) solution is proposed based on using the solution of the relaxed synchronous problem to obtain the solution to the asynchronous problem. The proposed HA asynchronous (HA-Asyn) approach is compared against the HA synchronous (HA-Sync) scheme and the heterogeneity unaware (HU) equal batch allocation scheme. Results from a system of 20 learners tested for various completion time and energy consumption constraints show that the proposed HA-Asyn method works better than the HU synchronous/asynchronous (HU-Sync/Asyn) approach and can provide gains of up-to 25% compared to the HA-Sync scheme.

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A Joint Learning and Communications Framework for Federated Learning Over Wireless Networks

Cited by 1,089 publications

References 24 publications

Privacy in the Cloud: A Survey of Existing Solutions and Research Challenges

Privacy in the Cloud: A Survey of Existing Solutions and Research Challenges

A Distributed Pulse-Based Synchronization Protocol for Half-Duplex D2D Communications

Task Allocation for Mobile Federated and Offloaded Learning with Energy and Delay Constraints

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