ICDE

2024

• Clients Help Clients: Alternating Collaboration for Semi-Supervised Federated Learning.

• Label Noise Correction for Federated Learning: A Secure, Efficient and Reliable Realization.

• HeteFedRec: Federated Recommender Systems with Model Heterogeneity.

• FedCross: Towards Accurate Federated Learning via Multi-Model Cross-Aggregation.

• RobFL: Robust Federated Learning via Feature Center Separation and Malicious Center Detection.

• Feed: Towards Personalization-Effective Federated Learning.

• Preventing the Popular Item Embedding Based Attack in Federated Recommendations.

• Semi-Asynchronous Online Federated Crowdsourcing.

• AdaFGL: A New Paradigm for Federated Node Classification with Topology Heterogeneity.

2023

• FedKNOW: Federated Continual Learning with Signature Task Knowledge Integration at Edge.

• Federated IoT Interaction Vulnerability Analysis.

• Dynamic Activation of Clients and Parameters for Federated Learning over Heterogeneous Graphs.

• Lumos: Heterogeneity-aware Federated Graph Learning over Decentralized Devices.

• Distribution-Regularized Federated Learning on Non-IID Data.

• Enhancing Decentralized Federated Learning for Non-IID Data on Heterogenous Devices.

• Fed-SC: One-Shot Federated Subspace Clustering over High-Dimensional Data.