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Found 8 papers, 1 papers with code
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.
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.
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.
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.
