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06/30/2023
High-throughput Simulation of Federated Learning via Resource-Aware Client Placement
Federated Learning (FL) is the privacy-preserving machine learning parad...
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06/06/2023
FedVal: Different good or different bad in federated learning
Federated learning (FL) systems are susceptible to attacks from maliciou...
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05/26/2023
vFedSec: Efficient Secure Aggregation for Vertical Federated Learning via Secure Layer
Most work in privacy-preserving federated learning (FL) has been focusin...
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05/22/2023
EXACT: Extensive Attack for Split Learning
Privacy-Preserving machine learning (PPML) can help us train and deploy ...
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05/18/2023
Efficient Vertical Federated Learning with Secure Aggregation
The majority of work in privacy-preserving federated learning (FL) has b...
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04/15/2023
Gradient-less Federated Gradient Boosting Trees with Learnable Learning Rates
The privacy-sensitive nature of decentralized datasets and the robustnes...
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08/04/2022
ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsity
When the available hardware cannot meet the memory and compute requireme...
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07/03/2022
Protea: Client Profiling within Federated Systems using Flower
Federated Learning (FL) has emerged as a prospective solution that facil...
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04/07/2021
On-device Federated Learning with Flower
Federated Learning (FL) allows edge devices to collaboratively learn a s...
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10/13/2020
A first look into the carbon footprint of federated learning
Despite impressive results, deep learning-based technologies also raise ...
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07/28/2020
Flower: A Friendly Federated Learning Research Framework
Federated Learning (FL) has emerged as a promising technique for edge de...
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05/18/2020
