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Found 15 papers, 2 papers with code
Brain Storm Optimization Based Swarm Learning for Diabetic Retinopathy Image Classification
Federated learning, as a privacy-preserving machine learning architecture, has shown promising performance in balancing data privacy and model utility by keeping private data on the client's side and using a central server to coordinate a set of clients for model training through aggregating their uploaded model parameters.
Automated Similarity Metric Generation for Recommendation
Given the critical role of similarity metrics in recommender systems, existing methods mainly employ handcrafted similarity metrics to capture the complex characteristics of user-item interactions.
Poisoning Decentralized Collaborative Recommender System and Its Countermeasures
Knowledge sharing also opens a backdoor for model poisoning attacks, where adversaries disguise themselves as benign clients and disseminate polluted knowledge to achieve malicious goals like promoting an item's exposure rate.
Robust Federated Contrastive Recommender System against Model Poisoning Attack
In this paper, we introduce a novel contrastive learning framework designed to fully leverage the client's sparse data through embedding augmentation, referred to as CL4FedRec.
Towards Personalized Privacy: User-Governed Data Contribution for Federated Recommendation
To bridge this gap, this paper explores a user-governed data contribution federated recommendation architecture where users are free to take control of whether they share data and the proportion of data they share to the server.
Decentralized Collaborative Learning with Adaptive Reference Data for On-Device POI Recommendation
Collaborative Learning (CL) emerges to promote model sharing among users, where reference data is an intermediary that allows users to exchange their soft decisions without directly sharing their private data or parameters, ensuring privacy and benefiting from collaboration.
On-Device Recommender Systems: A Comprehensive Survey
Recently, driven by the advances in storage, communication, and computation capabilities of edge devices, there has been a shift of focus from CloudRSs to on-device recommender systems (DeviceRSs), which leverage the capabilities of edge devices to minimize centralized data storage requirements, reduce the response latency caused by communication overheads, and enhance user privacy and security by localizing data processing and model training.
On-Device Recommender Systems: A Tutorial on The New-Generation Recommendation Paradigm
Given the sheer volume of contemporary e-commerce applications, recommender systems (RSs) have gained significant attention in both academia and industry.
Hide Your Model: A Parameter Transmission-free Federated Recommender System
Existing FedRecs generally adhere to a learning protocol in which a central server shares a global recommendation model with clients, and participants achieve collaborative learning by frequently communicating the model's public parameters.
HeteFedRec: Federated Recommender Systems with Model Heterogeneity
Owing to the nature of privacy protection, federated recommender systems (FedRecs) have garnered increasing interest in the realm of on-device recommender systems.
Personalized Elastic Embedding Learning for On-Device Recommendation
Given a memory budget, PEEL efficiently generates PEEs by selecting embedding blocks with the largest weights, making it adaptable to dynamic memory budgets on devices.
Semi-decentralized Federated Ego Graph Learning for Recommendation
In light of this, we propose a semi-decentralized federated ego graph learning framework for on-device recommendations, named SemiDFEGL, which introduces new device-to-device collaborations to improve scalability and reduce communication costs and innovatively utilizes predicted interacted item nodes to connect isolated ego graphs to augment local subgraphs such that the high-order user-item collaborative information could be used in a privacy-preserving manner.
Single-shot Embedding Dimension Search in Recommender System
In order to alleviate the above issues, some works focus on automated embedding dimension search by formulating it as hyper-parameter optimization or embedding pruning problems.
AutoML for Deep Recommender Systems: A Survey
To tackle this problem, Automated Machine Learning (AutoML) is introduced to automatically search for the proper candidates for different parts of deep recommender systems.
ImGAGN:Imbalanced Network Embedding via Generative Adversarial Graph Networks
Imbalanced classification on graphs is ubiquitous yet challenging in many real-world applications, such as fraudulent node detection.
