Search Results for author:
Found 73 papers, 12 papers with code
WassFFed: Wasserstein Fair Federated Learning
However, the limited existing research on fairness in FL does not effectively address two key challenges, i. e., (CH1) Current methods fail to deal with the inconsistency between fair optimization results obtained with surrogate functions and fair classification results.
FOOGD: Federated Collaboration for Both Out-of-distribution Generalization and Detection
Federated learning (FL) is a promising machine learning paradigm that collaborates with client models to capture global knowledge.
Enhancing Attributed Graph Networks with Alignment and Uniformity Constraints for Session-based Recommendation
In this paper, we propose a model-agnostic framework, named AttrGAU (Attributed Graph Networks with Alignment and Uniformity Constraints), to bring the MIA's superiority into existing attribute-agnostic models, to improve their accuracy and robustness for recommendation.
Federated Graph Learning for Cross-Domain Recommendation
This module enhances target domain training by expanding the graph of the target domain to generate reliable domain attentions and fine-tunes the target model for improved negative knowledge filtering and more accurate predictions.
DIIT: A Domain-Invariant Information Transfer Method for Industrial Cross-Domain Recommendation
Finally, for improving the efficiency, we design a migrator to transfer the extracted information to the latest target domain model, which only need the target domain model for inference.
CURE4Rec: A Benchmark for Recommendation Unlearning with Deeper Influence
Specifically, we consider the deeper influence of unlearning on recommendation fairness and robustness towards data with varying impact levels.
Controllable Unlearning for Image-to-Image Generative Models via $\varepsilon$-Constrained Optimization
We reformulate the I2I generative model unlearning problem into a $\varepsilon$-constrained optimization problem and solve it with a gradient-based method to find optimal solutions for unlearning boundaries.
Rethinking the Representation in Federated Unsupervised Learning with Non-IID Data
The former indicates that representation collapse in local model will subsequently impact the global model and other local models.
Post-Training Attribute Unlearning in Recommender Systems
The first component is distinguishability loss, where we design a distribution-based measurement to make attribute labels indistinguishable from attackers.
Personalized Behavior-Aware Transformer for Multi-Behavior Sequential Recommendation
To tackle these challenges, we propose a Personalized Behavior-Aware Transformer framework (PBAT) for MBSR problem, which models personalized patterns and multifaceted sequential collaborations in a novel way to boost recommendation performance.
Learning Uniform Clusters on Hypersphere for Deep Graph-level Clustering
However, most existing graph clustering methods focus on node-level clustering, i. e., grouping nodes in a single graph into clusters.
Federated Learning for Short Text Clustering
The robust short text clustering module aims to train an effective short text clustering model with local data in each client.
Making Users Indistinguishable: Attribute-wise Unlearning in Recommender Systems
To address the PoT-AU problem in recommender systems, we design a two-component loss function that consists of i) distinguishability loss: making attribute labels indistinguishable from attackers, and ii) regularization loss: preventing drastic changes in the model that result in a negative impact on recommendation performance.
In-processing User Constrained Dominant Sets for User-Oriented Fairness in Recommender Systems
By combining the fairness loss with the original backbone model loss, we address the UOF issue and maintain the overall recommendation performance simultaneously.
Defending Label Inference Attacks in Split Learning under Regression Setting
As a privacy-preserving method for implementing Vertical Federated Learning, Split Learning has been extensively researched.
Joint Local Relational Augmentation and Global Nash Equilibrium for Federated Learning with Non-IID Data
In server, GNE reaches an agreement among inconsistent and discrepant model deviations from clients to server, which encourages the global model to update in the direction of global optimum without breaking down the clients optimization toward their local optimums.
Freshness or Accuracy, Why Not Both? Addressing Delayed Feedback via Dynamic Graph Neural Networks
In the model training, we propose a novel graph convolutional method named HLGCN, which leverages both high-pass and low-pass filters to deal with conversion and non-conversion relationships.
Decentralized Graph Neural Network for Privacy-Preserving Recommendation
The first stage builds a local inner-item hypergraph for each user and a global inter-user graph.
HyperFed: Hyperbolic Prototypes Exploration with Consistent Aggregation for Non-IID Data in Federated Learning
Firstly, HPTI in the server constructs uniformly distributed and fixed class prototypes, and shares them with clients to match class statistics, further guiding consistent feature representation for local clients.
Integration of Large Language Models and Federated Learning
As the parameter size of Large Language Models (LLMs) continues to expand, there is an urgent need to address the scarcity of high-quality data.
Federated Unlearning via Active Forgetting
To this end, we propose a novel federated unlearning framework based on incremental learning, which is independent of specific models and federated settings.
Federated Learning on Non-iid Data via Local and Global Distillation
In this work, we propose FedND: federated learning with noise distillation.
Reducing Communication for Split Learning by Randomized Top-k Sparsification
However, communication efficiency is still a crucial issue for split learning.
Robust Representation Learning with Reliable Pseudo-labels Generation via Self-Adaptive Optimal Transport for Short Text Clustering
To tackle the above issues, we propose a Robust Short Text Clustering (RSTC) model to improve robustness against imbalanced and noisy data.
PPGenCDR: A Stable and Robust Framework for Privacy-Preserving Cross-Domain Recommendation
Privacy-preserving cross-domain recommendation (PPCDR) refers to preserving the privacy of users when transferring the knowledge from source domain to target domain for better performance, which is vital for the long-term development of recommender systems.
Selective and Collaborative Influence Function for Efficient Recommendation Unlearning
In this paper, we first identify two main disadvantages of directly applying existing unlearning methods in the context of recommendation, i. e., (i) unsatisfactory efficiency for large-scale recommendation models and (ii) destruction of collaboration across users and items.
UKnow: A Unified Knowledge Protocol with Multimodal Knowledge Graph Datasets for Reasoning and Vision-Language Pre-Training
This work presents a unified knowledge protocol, called UKnow, which facilitates knowledge-based studies from the perspective of data.
DCMT: A Direct Entire-Space Causal Multi-Task Framework for Post-Click Conversion Estimation
In recommendation scenarios, there are two long-standing challenges, i. e., selection bias and data sparsity, which lead to a significant drop in prediction accuracy for both Click-Through Rate (CTR) and post-click Conversion Rate (CVR) tasks.
INCREASE: Inductive Graph Representation Learning for Spatio-Temporal Kriging
It aims to infer knowledge for (the things at) unobserved locations using the data from (the things at) observed locations during a given time period of interest.
Heterogeneous Information Crossing on Graphs for Session-based Recommender Systems
HICG utilizes multiple types of user behaviors in the sessions to construct heterogeneous graphs, and captures users' current interests with their long-term preferences by effectively crossing the heterogeneous information on the graphs.
Protecting Split Learning by Potential Energy Loss
In this paper, we focus on the privacy leakage from the forward embeddings of split learning.
DDGHM: Dual Dynamic Graph with Hybrid Metric Training for Cross-Domain Sequential Recommendation
However, the short interaction sequences limit the performance of existing SR. To solve this problem, we focus on Cross-Domain Sequential Recommendation (CDSR) in this paper, which aims to leverage information from other domains to improve the sequential recommendation performance of a single domain.
Cross-Network Social User Embedding with Hybrid Differential Privacy Guarantees
Integrating multiple online social networks (OSNs) has important implications for many downstream social mining tasks, such as user preference modelling, recommendation, and link prediction.
Scalable and Sparsity-Aware Privacy-Preserving K-means Clustering with Application to Fraud Detection
The second type is provable secure but is inefficient and even helpless for the large-scale data sparsity scenario.
HCFRec: Hash Collaborative Filtering via Normalized Flow with Structural Consensus for Efficient Recommendation
The ever-increasing data scale of user-item interactions makes it challenging for an effective and efficient recommender system.
A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection
Deep graph learning has achieved remarkable progresses in both business and scientific areas ranging from finance and e-commerce, to drug and advanced material discovery.
Exploiting Variational Domain-Invariant User Embedding for Partially Overlapped Cross Domain Recommendation
Most of the existing CDR models assume that both the source and target domains share the same overlapped user set for knowledge transfer.
Making Recommender Systems Forget: Learning and Unlearning for Erasable Recommendation
The basic idea of most recommender systems is collaborative filtering, but existing MU methods ignore the collaborative information across users and items.
Exploiting Data Sparsity in Secure Cross-Platform Social Recommendation
As a result, our model can not only improve the recommendation performance of the rating platform by incorporating the sparse social data on the social platform, but also protect data privacy of both platforms.
Differential Private Knowledge Transfer for Privacy-Preserving Cross-Domain Recommendation
To this end, PriCDR can not only protect the data privacy of the source domain, but also alleviate the data sparsity of the source domain.
Collaborative Filtering with Attribution Alignment for Review-based Non-overlapped Cross Domain Recommendation
In this paper, we focus on the Review-based Non-overlapped Recommendation (RNCDR) problem.
Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback
As a result, client sampling plays an important role in FL systems as it affects the convergence rate of optimization algorithms used to train machine learning models.
Leveraging Distribution Alignment via Stein Path for Cross-Domain Cold-Start Recommendation
To address this issue, we propose DisAlign, a cross-domain recommendation framework for the CDCSR problem, which utilizes both rating and auxiliary representations from the source domain to improve the recommendation performance of the target domain.
A Unified Framework for Cross-Domain and Cross-System Recommendations
Moreover, to avoid negative transfer, we further propose a Personalized training strategy to minimize the embedding difference of common entities between a richer dataset and a sparser dataset, deriving three new models, i. e., GA-DTCDR-P, GA-MTCDR-P, and GA-CDR+CSR-P, for the three scenarios respectively.
Towards Secure and Practical Machine Learning via Secret Sharing and Random Permutation
Since our method reduces the cost for element-wise function computation, it is more efficient than existing cryptographic methods.
Cross-Domain Recommendation: Challenges, Progress, and Prospects
To address the long-standing data sparsity problem in recommender systems (RSs), cross-domain recommendation (CDR) has been proposed to leverage the relatively richer information from a richer domain to improve the recommendation performance in a sparser domain.
Towards Scalable and Privacy-Preserving Deep Neural Network via Algorithmic-Cryptographic Co-design
In this paper, we propose SPNN - a Scalable and Privacy-preserving deep Neural Network learning framework, from algorithmic-cryptographic co-perspective.
Improving Federated Relational Data Modeling via Basis Alignment and Weight Penalty
Federated learning (FL) has attracted increasing attention in recent years.
Survey and Open Problems in Privacy Preserving Knowledge Graph: Merging, Query, Representation, Completion and Applications
In this paper, to fill this gap, we summarize the open problems for privacy preserving KG in data isolation setting and propose possible solutions for them.
ASFGNN: Automated Separated-Federated Graph Neural Network
Specifically, to solve the data Non-IID problem, we first propose a separated-federated GNN learning model, which decouples the training of GNN into two parts: the message passing part that is done by clients separately, and the loss computing part that is learnt by clients federally.
A Deep Framework for Cross-Domain and Cross-System Recommendations
Therefore, finding an accurate mapping of the latent factors across domains or systems is crucial to enhancing recommendation accuracy.
When Homomorphic Encryption Marries Secret Sharing: Secure Large-Scale Sparse Logistic Regression and Applications in Risk Control
In this paper, we first present CAESAR, which combines HE and SS to build secure large-scale sparse logistic regression model and achieves both efficiency and security.
Vertically Federated Graph Neural Network for Privacy-Preserving Node Classification
Recently, Graph Neural Network (GNN) has achieved remarkable progresses in various real-world tasks on graph data, consisting of node features and the adjacent information between different nodes.
Large-Scale Secure XGB for Vertical Federated Learning
Privacy-preserving machine learning has drawn increasingly attention recently, especially with kinds of privacy regulations come into force.
Secret Sharing based Secure Regressions with Applications
Nowadays, the utilization of the ever expanding amount of data has made a huge impact on web technologies while also causing various types of security concerns.
Privacy Preserving Point-of-interest Recommendation Using Decentralized Matrix Factorization
However, existing MF approaches suffer from two major problems: (1) Expensive computations and storages due to the centralized model training mechanism: the centralized learners have to maintain the whole user-item rating matrix, and potentially huge low rank matrices.
Industrial Scale Privacy Preserving Deep Neural Network
Deep Neural Network (DNN) has been showing great potential in kinds of real-world applications such as fraud detection and distress prediction.
Practical Privacy Preserving POI Recommendation
Meanwhile, the public data need to be accessed by all the users are kept by the recommender to reduce the storage costs of users' devices.
Semi-supervised Learning Meets Factorization: Learning to Recommend with Chain Graph Model
The proposed CGM is a combination of Bayesian network and Markov random field.
Adapted tree boosting for Transfer Learning
Secure online transaction is an essential task for e-commerce platforms.
Heterogeneous Graph Neural Networks for Malicious Account Detection
We present, GEM, the first heterogeneous graph neural network approach for detecting malicious accounts at Alipay, one of the world's leading mobile cashless payment platform.
How Much Can A Retailer Sell? Sales Forecasting on Tmall
By analyzing the data, we have two main observations, i. e., sales seasonality after we group different groups of retails and a Tweedie distribution after we transform the sales (target to forecast).
Privacy Preserving PCA for Multiparty Modeling
In this paper, we present a general multiparty modeling paradigm with Privacy Preserving Principal Component Analysis (PPPCA) for horizontally partitioned data.
Secure Social Recommendation based on Secret Sharing
It is well known that social information, which is rich on social platforms such as Facebook, are useful to recommender systems.
A Time Attention based Fraud Transaction Detection Framework
With online payment platforms being ubiquitous and important, fraud transaction detection has become the key for such platforms, to ensure user account safety and platform security.
Quantification of the Leakage in Federated Learning
In this paper, we discuss the leakage based on a federated approximated logistic regression model and show that such gradient's leakage could leak the complete training data if all elements of the inputs are either 0 or 1.
Cryptography and Security
Characterizing Membership Privacy in Stochastic Gradient Langevin Dynamics
Based on this framework, we demonstrate that SGLD can prevent the information leakage of the training dataset to a certain extent.
Generalization in Generative Adversarial Networks: A Novel Perspective from Privacy Protection
In this paper, we aim to understand the generalization properties of generative adversarial networks (GANs) from a new perspective of privacy protection.
FinBrain: When Finance Meets AI 2.0
Artificial intelligence (AI) is the core technology of technological revolution and industrial transformation.
Distributed Deep Forest and its Application to Automatic Detection of Cash-out Fraud
This model can block fraud transactions in a large amount of money each day.
Distributed Collaborative Hashing and Its Applications in Ant Financial
The experimental results demonstrate that, comparing with the classic and state-of-the-art (distributed) latent factor models, DCH has comparable performance in terms of recommendation accuracy but has both fast convergence speed in offline model training procedure and realtime efficiency in online recommendation procedure.
Time-sensitive Customer Churn Prediction based on PU Learning
With the fast development of Internet companies throughout the world, customer churn has become a serious concern.
GeniePath: Graph Neural Networks with Adaptive Receptive Paths
We present, GeniePath, a scalable approach for learning adaptive receptive fields of neural networks defined on permutation invariant graph data.
