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Found 6 papers, 1 papers with code
RVFR: Robust Vertical Federated Learning via Feature Subspace Recovery
Vertical Federated Learning (VFL) is a distributed learning paradigm that allows multiple agents to jointly train a global model when each agent holds a different subset of features for the same sample(s).
SABAL: Sparse Approximation-based Batch Active Learning
We propose a novel and general framework (i. e., SABAL) that formulates batch active learning as a sparse approximation problem.
Scalable Robust Federated Learning with Provable Security Guarantees
Federated averaging, the most popular aggregation approach in federated learning, is known to be vulnerable to failures and adversarial updates from clients that wish to disrupt training.
Robust and Personalized Federated Learning with Spurious Features: an Adversarial Approach
To this end, we propose a strategy to mitigate the effect of spurious features based on our observation that the global model in the federated learning step has a low accuracy disparity due to statistical heterogeneity.
Identifying Coarse-grained Independent Causal Mechanisms with Self-supervision
Current approaches for learning disentangled representations assume that independent latent variables generate the data through a single data generation process.
Does Adversarial Transferability Indicate Knowledge Transferability?
Despite the immense success that deep neural networks (DNNs) have achieved, \emph{adversarial examples}, which are perturbed inputs that aim to mislead DNNs to make mistakes, have recently led to great concerns.
