Due to underlying privacy-sensitive information in user-item interaction data, the risk of
privacy leakage exists in the centralized-training recommender system (RecSys). To this …

Federated learning (FL) algorithm has been widely studied in recent years due to its ability
for sharing data while protecting privacy. However, FL has risks, such as model inversion …

Federated learning (FL) enables the collaborative training of machine learning models
without sharing training data. Traditional FL heavily relies on a trusted centralized server …

Federated learning facilitates collaborative learning among multiple parties while ensuring
client privacy. The vulnerability of federated learning to diverse Byzantine attacks stems from …

Federated learning (FL) is widely employed for collaborative training on decentralized data
but faces challenges like data, system, and model heterogeneity. This prompted the …

The proliferation of IoT devices has led to an unprecedented integration of machine learning
techniques, raising concerns about data privacy. To address these concerns, federated …

Existing approaches defend against backdoor attacks in federated learning (FL) mainly
through a) mitigating the impact of infected models, or b) excluding infected models. The …

Abstract Federated Graph Learning (FGL) opens up new possibilities for machine learning
in complex networks and distributed training, enabling multiple clients to collaborate on …

Federated Learning (FL) has emerged as a fundamental learning paradigm to harness
massive data scattered at geo-distributed edge devices in a privacy-preserving way. Given …

Collaborative fairness stands as an essential element in federated learning to encourage
client participation by equitably distributing rewards based on individual contributions …