Byzantine-robust decentralized federated learning
Federated learning (FL) enables multiple clients to collaboratively train machine learning
models without revealing their private training data. In conventional FL, the system follows …
models without revealing their private training data. In conventional FL, the system follows …
Poisoning Attacks on Federated Learning-based Wireless Traffic Prediction
Federated Learning (FL) offers a distributed framework to train a global control model across
multiple base stations without compromising the privacy of their local network data. This …
multiple base stations without compromising the privacy of their local network data. This …
Eyes on Federated Recommendation: Targeted Poisoning With Competition and Its Mitigation
Federated recommendation (FR) addresses privacy concerns in recommender systems by
training a global model without requiring raw user data to leave individual devices. A server …
training a global model without requiring raw user data to leave individual devices. A server …
A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research
Recommender systems are essential for personalizing digital experiences on e-commerce
sites, streaming services, and social media platforms. While these systems are necessary for …
sites, streaming services, and social media platforms. While these systems are necessary for …
PRSI: Privacy-Preserving Recommendation Model Based on Vector Splitting and Interactive Protocols
X Cao, W Mo, Z He, C Wang - arXiv preprint arXiv:2411.18653, 2024 - arxiv.org
With the development of the internet, recommending interesting products to users has
become a highly valuable research topic for businesses. Recommendation systems play a …
become a highly valuable research topic for businesses. Recommendation systems play a …
The Role of Fake Users in Sequential Recommender Systems
F Betello - arXiv preprint arXiv:2410.09936, 2024 - arxiv.org
Sequential Recommender Systems (SRSs) are widely used to model user behavior over
time, yet their robustness remains an under-explored area of research. In this paper, we …
time, yet their robustness remains an under-explored area of research. In this paper, we …
FedREDefense: Defending against Model Poisoning Attacks for Federated Learning using Model Update Reconstruction Error
Federated Learning (FL) faces threats from model poisoning attacks. Existing defenses,
typically relying on cross-client/global information to mitigate these attacks, fall short when …
typically relying on cross-client/global information to mitigate these attacks, fall short when …