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Found 29 papers, 8 papers with code
FedBRB: An Effective Solution to the Small-to-Large Scenario in Device-Heterogeneity Federated Learning
FedBRB can uses small local models to train all blocks of the large global model, and broadcasts the trained parameters to the entire space for faster information interaction.
Clients Collaborate: Flexible Differentially Private Federated Learning with Guaranteed Improvement of Utility-Privacy Trade-off
This implies that our FedCEO can effectively recover the disrupted semantic information by smoothing the global semantic space for different privacy settings and continuous training processes.
VeryFL: A Verify Federated Learning Framework Embedded with Blockchain
These mechanism on blockchain shows an underlying support of blockchain for federated learning to provide a verifiable training, aggregation and incentive distribution procedure and thus we named this framework VeryFL (A Verify Federated Learninig Framework Embedded with Blockchain).
Tokenized Model: A Blockchain-Empowered Decentralized Model Ownership Verification Platform
By using the model watermarking technology, we point out the possibility of building a unified platform for model ownership verification.
Community-Aware Efficient Graph Contrastive Learning via Personalized Self-Training
However, for unsupervised and structure-related tasks such as community detection, current GCL algorithms face difficulties in acquiring the necessary community-level information, resulting in poor performance.
Contrastive Deep Nonnegative Matrix Factorization for Community Detection
Recently, nonnegative matrix factorization (NMF) has been widely adopted for community detection, because of its better interpretability.
Ranked #1 on
Community Detection
on Pubmed
Multi-Agent Deep Reinforcement Learning for Dynamic Avatar Migration in AIoT-enabled Vehicular Metaverses with Trajectory Prediction
Specifically, we propose a model to predict the future trajectories of intelligent vehicles based on their historical data, indicating the future workloads of RSUs. Based on the expected workloads of RSUs, we formulate the avatar task migration problem as a long-term mixed integer programming problem.
DsMtGCN: A Direction-sensitive Multi-task framework for Knowledge Graph Completion
To solve the inherent incompleteness of knowledge graphs (KGs), numbers of knowledge graph completion (KGC) models have been proposed to predict missing links from known triples.
Contextual Dictionary Lookup for Knowledge Graph Completion
We extend several KGE models with the method, resulting in substantial performance improvements on widely-used benchmark datasets.
Migrate Demographic Group For Fair GNNs
To tackle this problem, we propose a brand new framework, FairMigration, which can dynamically migrate the demographic groups instead of keeping that fixed with raw sensitive attributes.
Challenges and Remedies to Privacy and Security in AIGC: Exploring the Potential of Privacy Computing, Blockchain, and Beyond
Artificial Intelligence Generated Content (AIGC) is one of the latest achievements in AI development.
Capturing Fine-grained Semantics in Contrastive Graph Representation Learning
Concretely, FSGCL first introduces a motif-based graph construction, which employs graph motifs to extract diverse semantics existed in graphs from the perspective of input data.
Decoupling anomaly discrimination and representation learning: self-supervised learning for anomaly detection on attributed graph
DSLAD is a self-supervised method with anomaly discrimination and representation learning decoupled for anomaly detection.
FedEgo: Privacy-preserving Personalized Federated Graph Learning with Ego-graphs
As special information carriers containing both structure and feature information, graphs are widely used in graph mining, e. g., Graph Neural Networks (GNNs).
Distributed Evolution Strategies for Black-box Stochastic Optimization
This work concerns the evolutionary approaches to distributed stochastic black-box optimization, in which each worker can individually solve an approximation of the problem with nature-inspired algorithms.
A Decentralized Federated Learning Framework via Committee Mechanism with Convergence Guarantee
In addition, we theoretically analyze and prove the convergence of CMFL under different election and selection strategies, which coincides with the experimental results.
A Dual-Purpose Deep Learning Model for Auscultated Lung and Tracheal Sound Analysis Based on Mixed Set Training
However, mixed set training or domain adaptation improved the performance for 1) inhalation and exhalation detection in lung sounds and 2) inhalation, exhalation, and CAS detection in tracheal sounds compared to positive controls (the models trained using lung sound alone and used in lung sound analysis and vice versa).
FedGL: Federated Graph Learning Framework with Global Self-Supervision
Moreover, the global self-supervision enables the information of each client to flow and share in a privacy-preserving manner, thus alleviating the heterogeneity and utilizing the complementarity of graph data among different clients.
A_Blockchain-Based_Decentralized_Federated_Learning_Framework_with_Committee_Consensus
To address these security issues, we propose a decentralized federated learning framework based on blockchain, that is, a Blockchain- based Federated Learning framework with Committee consensus (BFLC).
Tensor Completion via Convolutional Sparse Coding Regularization
On the contrary, in the field of signal processing, Convolutional Sparse Coding (CSC) can provide a good representation of the high-frequency component of the image, which is generally associated with the detail component of the data.
Competition of Spinon Fermi Surface and Heavy Fermi Liquids states from the Periodic Anderson to the Hubbard model
We study a model of correlated electrons coupled by tunnelling to a layer of itinerant metallic electrons, which allows to interpolate from a frustrated limit favorable to spin liquid states to a Kondo-lattice limit favorable to interlayer coherent heavy metallic states.
Strongly Correlated Electrons Materials Science
Outlier-Resilient Web Service QoS Prediction
The proliferation of Web services makes it difficult for users to select the most appropriate one among numerous functionally identical or similar service candidates.
SocialTrans: A Deep Sequential Model with Social Information for Web-Scale Recommendation Systems
Experiments on three data sets verify the effectiveness of our model and show that it outperforms state-of-the-art social recommendation methods.
A Blockchain-based Decentralized Federated Learning Framework with Committee Consensus
To address these security issues, we proposed a decentralized federated learning framework based on blockchain, i. e., a Blockchain-based Federated Learning framework with Committee consensus (BFLC).
An Uncoupled Training Architecture for Large Graph Learning
Instead of directly processing the coupled nodes as GCNs, Node2Grids supports a more efficacious method in practice, mapping the coupled graph data into the independent grid-like data which can be fed into the efficient Convolutional Neural Network (CNN).
LoCEC: Local Community-based Edge Classification in Large Online Social Networks
To tackle the challenges, we propose a Local Community-based Edge Classification (LoCEC) framework that classifies user relationships in a social network into real-world social connection types.
Deep Autoencoder-like Nonnegative Matrix Factorization for Community Detection
Considering the complicated and diversified topology structures of real-world networks, it is highly possible that the mapping between the original network and the community membership space contains rather complex hierarchical information, which cannot be interpreted by classic shallow NMF-based approaches.
Ranked #1 on
Node Classification
on Wiki
Collaborative Deep Learning Across Multiple Data Centers
Valuable training data is often owned by independent organizations and located in multiple data centers.
Learning Semantic Representations for Unsupervised Domain Adaptation
Prior domain adaptation methods address this problem through aligning the global distribution statistics between source domain and target domain, but a drawback of prior methods is that they ignore the semantic information contained in samples, e. g., features of backpacks in target domain might be mapped near features of cars in source domain.
Ranked #8 on
Domain Adaptation
on SVHN-to-MNIST
Learning Semantic Representations
Unsupervised Domain Adaptation
