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Found 6 papers, 1 papers with code
On the resilience of Collaborative Learning-based Recommender Systems Against Community Detection Attack
This attack enables an adversary to identify community members based on a chosen set of items (eg., identifying users interested in specific points-of-interest).
Survey of Federated Learning Models for Spatial-Temporal Mobility Applications
Federated learning involves training statistical models over edge devices such as mobile phones such that the training data is kept local.
Shielding Federated Learning Systems against Inference Attacks with ARM TrustZone
Specifically, in FL, models are trained on the users devices and only model updates (i. e., gradients) are sent to a central server for aggregation purposes.
PEPPER: Empowering User-Centric Recommender Systems over Gossip Learning
To remedy this, we propose PEPPER, a decentralized recommender system based on gossip learning principles.
Enhancing Robustness of On-line Learning Models on Highly Noisy Data
Classification algorithms have been widely adopted to detect anomalies for various systems, e. g., IoT, cloud and face recognition, under the common assumption that the data source is clean, i. e., features and labels are correctly set.
RAD: On-line Anomaly Detection for Highly Unreliable Data
Classification algorithms have been widely adopted to detect anomalies for various systems, e. g., IoT, cloud and face recognition, under the common assumption that the data source is clean, i. e., features and labels are correctly set.
