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Found 10 papers, 2 papers with code
WaveAttack: Asymmetric Frequency Obfuscation-based Backdoor Attacks Against Deep Neural Networks
Due to the popularity of Artificial Intelligence (AI) technology, numerous backdoor attacks are designed by adversaries to mislead deep neural network predictions by manipulating training samples and training processes.
EqGAN: Feature Equalization Fusion for Few-shot Image Generation
Due to the absence of fine structure and texture information, existing fusion-based few-shot image generation methods suffer from unsatisfactory generation quality and diversity.
FedMR: Federated Learning via Model Recombination
Although Federated Learning (FL) enables global model training across clients without compromising their raw data, existing Federated Averaging (FedAvg)-based methods suffer from the problem of low inference performance, especially for unevenly distributed data among clients.
HierarchyFL: Heterogeneous Federated Learning via Hierarchical Self-Distillation
Federated learning (FL) has been recognized as a privacy-preserving distributed machine learning paradigm that enables knowledge sharing among various heterogeneous artificial intelligence (AIoT) devices through centralized global model aggregation.
GitFL: Adaptive Asynchronous Federated Learning using Version Control
Unlike traditional FL, the cloud server of GitFL maintains a master model (i. e., the global model) together with a set of branch models indicating the trained local models committed by selected devices, where the master model is updated based on both all the pushed branch models and their version information, and only the branch models after the pull operation are dispatched to devices.
FedCross: Towards Accurate Federated Learning via Multi-Model Cross Aggregation
Due to the remarkable performance in preserving data privacy for decentralized data scenarios, Federated Learning (FL) has been considered as a promising distributed machine learning paradigm to deal with data silos problems.
FedMR: Fedreated Learning via Model Recombination
Worse still, in each round of FL training, FedAvg dispatches the same initial local models to clients, which can easily result in stuck-at-local-search for optimal global models.
FedEntropy: Efficient Device Grouping for Federated Learning Using Maximum Entropy Judgment
Along with the popularity of Artificial Intelligence (AI) and Internet-of-Things (IoT), Federated Learning (FL) has attracted steadily increasing attentions as a promising distributed machine learning paradigm, which enables the training of a central model on for numerous decentralized devices without exposing their privacy.
Model-Contrastive Learning for Backdoor Defense
Due to the popularity of Artificial Intelligence (AI) techniques, we are witnessing an increasing number of backdoor injection attacks that are designed to maliciously threaten Deep Neural Networks (DNNs) causing misclassification.
FedCAT: Towards Accurate Federated Learning via Device Concatenation
As a promising distributed machine learning paradigm, Federated Learning (FL) enables all the involved devices to train a global model collaboratively without exposing their local data privacy.
