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10/03/2022
Taming Fat-Tailed ("Heavier-Tailed” with Potentially Infinite Variance) Noise in Federated Learning
A key assumption in most existing works on FL algorithms' convergence an...
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10/02/2022
SAGDA: Achieving 𝒪(ε^-2) Communication Complexity in Federated Min-Max Learning
To lower the communication complexity of federated min-max learning, a n...
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09/28/2022
Denoising of 3D MR images using a voxel-wise hybrid residual MLP-CNN model to improve small lesion diagnostic confidence
Small lesions in magnetic resonance imaging (MRI) images are crucial for...
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08/17/2022
NET-FLEET: Achieving Linear Convergence Speedup for Fully Decentralized Federated Learning with Heterogeneous Data
Federated learning (FL) has received a surge of interest in recent years...
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05/19/2022
CHARLES: Channel-Quality-Adaptive Over-the-Air Federated Learning over Wireless Networks
Over-the-air federated learning (OTA-FL) has emerged as an efficient mec...
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05/12/2022
Over-the-Air Federated Learning with Joint Adaptive Computation and Power Control
This paper considers over-the-air federated learning (OTA-FL). OTA-FL ex...
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08/23/2021
Anarchic Federated Learning
Present-day federated learning (FL) systems deployed over edge networks ...
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06/19/2021
STEM: A Stochastic Two-Sided Momentum Algorithm Achieving Near-Optimal Sample and Communication Complexities for Federated Learning
Federated Learning (FL) refers to the paradigm where multiple worker nod...
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06/14/2021
CFedAvg: Achieving Efficient Communication and Fast Convergence in Non-IID Federated Learning
Federated learning (FL) is a prevailing distributed learning paradigm, w...
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01/27/2021
Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning
Federated learning (FL) is a distributed machine learning architecture t...
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09/10/2019
