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09/18/2023
FedLALR: Client-Specific Adaptive Learning Rates Achieve Linear Speedup for Non-IID Data
Federated learning is an emerging distributed machine learning method, e...
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03/28/2023
Communication-Efficient Vertical Federated Learning with Limited Overlapping Samples
Federated learning is a popular collaborative learning approach that ena...
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03/28/2023
Boundary-to-Solution Mapping for Groundwater Flows in a Toth Basin
In this paper, the authors propose a new approach to solving the groundw...
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03/01/2023
AdaSAM: Boosting Sharpness-Aware Minimization with Adaptive Learning Rate and Momentum for Training Deep Neural Networks
Sharpness aware minimization (SAM) optimizer has been extensively explor...
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01/15/2023
Synthesizing Proxy Applications for MPI Programs
Proxy applications (proxy-apps) are basic tools for evaluating the perfo...
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11/11/2022
More Generalized and Personalized Unsupervised Representation Learning In A Distributed System
Discriminative unsupervised learning methods such as contrastive learnin...
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09/30/2022
Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reduction
Due to limited communication capacities of edge devices, most existing f...
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10/26/2021
FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspective
Federated learning (FL) is a popular distributed learning framework that...
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12/08/2020
Provable Defense against Privacy Leakage in Federated Learning from Representation Perspective
Federated learning (FL) is a popular distributed learning framework that...
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08/07/2020