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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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09/08/2022
FADE: Enabling Large-Scale Federated Adversarial Training on Resource-Constrained Edge Devices
Adversarial Training (AT) has been proven to be an effective method of i...
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03/30/2022
Towards Collaborative Intelligence: Routability Estimation based on Decentralized Private Data
Applying machine learning (ML) in design flow is a popular trend in EDA ...
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03/24/2021
FedGP: Correlation-Based Active Client Selection for Heterogeneous Federated Learning
Client-wise heterogeneity is one of the major issues that hinder effecti...
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04/20/2020
Learning Low-rank Deep Neural Networks via Singular Vector Orthogonality Regularization and Singular Value Sparsification
Modern deep neural networks (DNNs) often require high memory consumption...
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10/10/2019
