- Biscotti: A Blockchain System for Private and Secure Federated Learning [Paper]
- Mutual Information Driven Federated Learning [Paper]
- Accelerating Federated Learning over Reliability-Agnostic Clients in Mobile Edge Computing Systems [Paper]
- Self-Balancing Federated Learning With Global Imbalanced Data in Mobile Systems [Paper] [Github]
- Towards Efficient Scheduling of Federated Mobile Devices under Computational and Statistical Heterogeneity [Paper]
- An Efficiency-boosting Client Selection Scheme for Federated Learning with Fairness Guarantee [Paper]
- Robust and Communication-Efficient Federated Learning From Non-i.i.d. Data [Paper]
- Toward Communication-Efficient Federated Learning in the Internet of Things With Edge Computing [Paper]
- Communication-Efficient Federated Learning and Permissioned Blockchain for Digital Twin Edge Networks [Paper]
- CEFL: Online Admission Control, Data Scheduling, and Accuracy Tuning for Cost-Efficient Federated Learning Across Edge Nodes [Paper]
- Privacy-Preserving Federated Learning in Fog Computing [Paper]
- FedMCCS: Multicriteria Client Selection Model for Optimal IoT Federated Learning [Paper]
- Federated Deep Reinforcement Learning for Internet of Things With Decentralized Cooperative Edge Caching [Paper]
- FDC: A Secure Federated Deep Learning Mechanism for Data Collaborations in the Internet of Things [Paper]
- Personalized Federated Learning With Differential Privacy [Paper]
- Privacy-Preserving Traffic Flow Prediction: A Federated Learning Approach [Paper]
- Federated Sensing: Edge-Cloud Elastic Collaborative Learning for Intelligent Sensing [Paper]
- PoisonGAN: Generative Poisoning Attacks Against Federated Learning in Edge Computing Systems [Paper]