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Found 14 papers, 4 papers with code
Self-supervised Cross-silo Federated Neural Architecture Search
Federated Learning (FL) provides both model performance and data privacy for machine learning tasks where samples or features are distributed among different parties.
Backdoor attacks and defenses in feature-partitioned collaborative learning
In this paper, we show that even parties with no access to labels can successfully inject backdoor attacks, achieving high accuracy on both main and backdoor tasks.
Privacy-Preserving Technology to Help Millions of People: Federated Prediction Model for Stroke Prevention
It adopts federated averaging during the model training process, without patient data being taken out of the hospitals during the whole process of model training and forecasting.
Learning to Detect Malicious Clients for Robust Federated Learning
Federated learning systems are vulnerable to attacks from malicious clients.
RPN: A Residual Pooling Network for Efficient Federated Learning
Federated learning is a distributed machine learning framework which enables different parties to collaboratively train a model while protecting data privacy and security.
FedVision: An Online Visual Object Detection Platform Powered by Federated Learning
Federated learning (FL) is a promising approach to resolve this challenge.
A Communication Efficient Collaborative Learning Framework for Distributed Features
We introduce a collaborative learning framework allowing multiple parties having different sets of attributes about the same user to jointly build models without exposing their raw data or model parameters.
A Quasi-Newton Method Based Vertical Federated Learning Framework for Logistic Regression
Our approach can considerably reduce the number of communication rounds with a little additional communication cost per round.
Abnormal Client Behavior Detection in Federated Learning
In federated learning systems, clients are autonomous in that their behaviors are not fully governed by the server.
Federated Transfer Reinforcement Learning for Autonomous Driving
Reinforcement learning (RL) is widely used in autonomous driving tasks and training RL models typically involves in a multi-step process: pre-training RL models on simulators, uploading the pre-trained model to real-life robots, and fine-tuning the weight parameters on robot vehicles.
HHHFL: Hierarchical Heterogeneous Horizontal Federated Learning for Electroencephalography
To verify the effectiveness of our approach, we conduct experiments on a real-world EEG dataset, consisting of heterogeneous data collected from diverse devices.
Federated Machine Learning: Concept and Applications
We propose a possible solution to these challenges: secure federated learning.
SecureBoost: A Lossless Federated Learning Framework
This federated learning system allows the learning process to be jointly conducted over multiple parties with common user samples but different feature sets, which corresponds to a vertically partitioned data set.
Secure Federated Transfer Learning
A secure transfer cross validation approach is also proposed to guard the FTL performance under the federation.
