Improved convergence analysis and snr control strategies for federated learning in the presence of noise
A Upadhyay, A Hashemi - IEEE Access, 2023 - ieeexplore.ieee.org
We propose an improved convergence analysis technique that characterizes the distributed
learning paradigm of federated learning (FL) with imperfect/noisy uplink and downlink …
learning paradigm of federated learning (FL) with imperfect/noisy uplink and downlink …
Improved Convergence Analysis and SNR Control Strategies for Federated Learning in the Presence of Noise
A Upadhyay, A Hashemi - arXiv preprint arXiv:2307.07406, 2023 - arxiv.org
We propose an improved convergence analysis technique that characterizes the distributed
learning paradigm of federated learning (FL) with imperfect/noisy uplink and downlink …
learning paradigm of federated learning (FL) with imperfect/noisy uplink and downlink …
Improved Convergence Analysis and SNR Control Strategies for Federated Learning in the Presence of Noise
A Upadhyay, A Hashemi - arXiv e-prints, 2023 - ui.adsabs.harvard.edu
We propose an improved convergence analysis technique that characterizes the distributed
learning paradigm of federated learning (FL) with imperfect/noisy uplink and downlink …
learning paradigm of federated learning (FL) with imperfect/noisy uplink and downlink …
[引用][C] Improved Convergence Analysis and SNR Control Strategies for Federated Learning in the Presence of Noise
A Upadhyay, A Hashemi - IEEE Access, 2023 - ui.adsabs.harvard.edu
Improved Convergence Analysis and SNR Control Strategies for Federated Learning in the
Presence of Noise - NASA/ADS Now on home page ads icon ads Enable full ADS view …
Presence of Noise - NASA/ADS Now on home page ads icon ads Enable full ADS view …