Accelerated Gradient Descent Learning Over Multiple Access Fading Channels

Paul, Raz; Friedman, Y.; Cohen, Kobi

doi:10.1109/jsac.2021.3118410

Cited by 18 publications

(5 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As a result, communication latency of such edge networks will be improved [93]. For the guarantee of the URLLC service, authors in [94] have proposed an accelerated gradient-descent multiple access algorithm. The proposed algorithm has contributed to optimize the model accuracy and training speed.…”

Section: Federated Edge Learning (Feel) Algorithmmentioning

confidence: 99%

Resource Allocation for Co-Existence of eMBB and URLLC Services in 6G Wireless Networks: A Survey

Adhikari,

Jaseemuddin,

Anpalagan

2024

IEEE Access

View full text Add to dashboard Cite

Next generation of wireless networks are characterized by two main features named Enhanced Mobile Broadband (eMBB) and Ultra Reliable Low Latency Communications (URLLC). These two services can be accommodated in the same wireless infrastructure so that wide range of users, demanding either massive throughput or extremely low latency and high reliability requirements, are directly benefited for providing various mission critical services. Co-existence of eMBB and URLLC services, however, demand highly efficient and less complex resource allocation schemes. In this paper, various resource allocation techniques are studied for the co-existence of eMBB and URLLC traffic to meet the heterogeneous specifications of each class of users. A detailed study on existing resource allocation schemes for simultaneous transmission of eMBB and URLLC services based on network slicing, flexible Transmit Time Interval (TTI), scheduling and distributed and federated learning are provided. Moreover, Machine Learning (ML) aided and Reconfigurable Intelligent Surface (RIS) and UAV assisted resource allocation techniques are also studied in detail. Additionally, this paper identifies some challenges for eMBB and URLLC service accommodation in the same wireless architecture and proposes their possible solution approaches.INDEX TERMS eMBB and URLLC, multiplexing, resource allocation, slicing, transmit time interval, machine learning.

show abstract

Section: Federated Edge Learning (Feel) Algorithmmentioning

confidence: 99%

Resource Allocation for Co-Existence of eMBB and URLLC Services in 6G Wireless Networks: A Survey

Adhikari,

Jaseemuddin,

Anpalagan

2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Similar ideas have been applied to Single Input Single Output (SISO) fading channels [13], and to Multiple Input Multiple Output (MIMO) channels [14]. More recent algorithms in this area include those described in [15,16,17,18,19] and the references therein.…”

Section: A Federated Learning and Over-the-air Federated Learningmentioning

confidence: 99%

PolarAir: A Compressed Sensing Scheme for Over-the-Air Federated Learning

Gkagkos¹,

Narayanan²,

Jean-Francois³

et al. 2023

Preprint

View full text Add to dashboard Cite

We explore a scheme that enables the training of a deep neural network in a Federated Learning configuration over an additive white Gaussian noise channel. The goal is to create a low complexity, linear compression strategy, called PolarAir, that reduces the size of the gradient at the user side to lower the number of channel uses needed to transmit it. The suggested approach belongs to the family of compressed sensing techniques, yet it constructs the sensing matrix and the recovery procedure using multiple access techniques. Simulations show that it can reduce the number of channel uses by ∼30% when compared to conveying the gradient without compression. The main advantage of the proposed scheme over other schemes in the literature is its low time complexity. We also investigate the behavior of gradient updates and the performance of PolarAir throughout the training process to obtain insight on how best to construct this compression scheme based on compressed sensing.

show abstract

“…Channel communication characteristics have been studied further in [31]. In our previous work [30], [35], we have developed and analyzed gradient-based learning, and accelerated learning methods without using power control or beamforming to cancel the fading effect. In [28], the authors developed the federated edge learning algorithm that schedules entries of the gradient vector based on the channel condition.…”

Section: A Learning With Ota Computationmentioning

confidence: 99%

Subgradient Descent Learning Over Fading Multiple Access Channels With Over-the-Air Computation

Gez

Cohen

2023

IEEE Access

Self Cite

View full text Add to dashboard Cite

A short version of this paper that introduces the algorithm, and preliminary simulation results was accepted for presentation at the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2023 [1]. In this journal version we include: (i) A detailed discussion on the implementation of the algorithm; (ii) a rigorous theoretical analysis of the algorithm with detailed proofs; (iii) more extensive simulation results; and (iv) a detailed discussion of the results, and comprehensive discussion and comparison with the existing literature.

show abstract

Accelerated Gradient Descent Learning Over Multiple Access Fading Channels

Cited by 18 publications

References 41 publications

Resource Allocation for Co-Existence of eMBB and URLLC Services in 6G Wireless Networks: A Survey

Resource Allocation for Co-Existence of eMBB and URLLC Services in 6G Wireless Networks: A Survey

PolarAir: A Compressed Sensing Scheme for Over-the-Air Federated Learning

Subgradient Descent Learning Over Fading Multiple Access Channels With Over-the-Air Computation

Contact Info

Product

Resources

About