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Found 6 papers, 3 papers with code
Desirable Companion for Vertical Federated Learning: New Zeroth-Order Gradient Based Algorithm
Based on that, we propose a novel and practical VFL framework with black-box models, which is inseparably interconnected to the promising properties of ZOO.
SelfSAGCN: Self-Supervised Semantic Alignment for Graph Convolution Network
Specifically, the Identity Aggregation is applied to extract semantic features from labeled nodes, the Semantic Alignment is utilized to align node features obtained from different aspects using the class central similarity.
Nearest Neighbor Matching for Deep Clustering
Specifically, for the local level, we match the nearest neighbors based on batch embedded features, as for the global one, we match neighbors from overall embedded features.
Doubly Contrastive Deep Clustering
In this paper, we present a novel Doubly Contrastive Deep Clustering (DCDC) framework, which constructs contrastive loss over both sample and class views to obtain more discriminative features and competitive results.
Federated Doubly Stochastic Kernel Learning for Vertically Partitioned Data
In this paper, we focus on nonlinear learning with kernels, and propose a federated doubly stochastic kernel learning (FDSKL) algorithm for vertically partitioned data.
Multi-Scale Fusion Subspace Clustering Using Similarity Constraint
To tackle this issue, deep subspace clustering (DSC) networks based on deep autoencoder (DAE) have been proposed, which non-linearly map the raw form data into a latent space well-adapted to subspace clustering.
