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Found 9 papers, 4 papers with code
Efficiently Assemble Normalization Layers and Regularization for Federated Domain Generalization
Domain shift is a formidable issue in Machine Learning that causes a model to suffer from performance degradation when tested on unseen domains.
Towards Efficient Communication and Secure Federated Recommendation System via Low-rank Training
Federated Recommendation (FedRec) systems have emerged as a solution to safeguard users' data in response to growing regulatory concerns.
Fooling the Textual Fooler via Randomizing Latent Representations
Despite outstanding performance in a variety of NLP tasks, recent studies have revealed that NLP models are vulnerable to adversarial attacks that slightly perturb the input to cause the models to misbehave.
Understanding the Robustness of Randomized Feature Defense Against Query-Based Adversarial Attacks
Recent works have shown that deep neural networks are vulnerable to adversarial examples that find samples close to the original image but can make the model misclassify.
An Empirical Study of Federated Learning on IoT-Edge Devices: Resource Allocation and Heterogeneity
Nowadays, billions of phones, IoT and edge devices around the world generate data continuously, enabling many Machine Learning (ML)-based products and applications.
FedGrad: Mitigating Backdoor Attacks in Federated Learning Through Local Ultimate Gradients Inspection
Federated learning (FL) enables multiple clients to train a model without compromising sensitive data.
Backdoor Attacks and Defenses in Federated Learning: Survey, Challenges and Future Research Directions
Federated learning (FL) is a machine learning (ML) approach that allows the use of distributed data without compromising personal privacy.
Personalized Privacy-Preserving Framework for Cross-Silo Federated Learning
Federated learning (FL) is recently surging as a promising decentralized deep learning (DL) framework that enables DL-based approaches trained collaboratively across clients without sharing private data.
FedDCT: Federated Learning of Large Convolutional Neural Networks on Resource Constrained Devices using Divide and Collaborative Training
FedDCT reduces the memory requirements and allows low-end devices to participate in FL.
