Byzantine Tolerant Algorithms for Federated Learning.
IEEE Trans. Netw. Sci. Eng., 2023
An Efficient and Robust Cloud-Based Deep Learning With Knowledge Distillation.
IEEE Trans. Cloud Comput., 2023
Preconditioned Federated Learning.
CoRR, 2023
LAWS: Look Around and Warm-Start Natural Gradient Descent for Quantum Neural Networks.
Proceedings of the IEEE International Conference on Quantum Software, 2023
Scalable Quantum Neural Networks for Classification.
CoRR, 2022
wpScalable Quantum Neural Networks for Classification.
Proceedings of the IEEE International Conference on Quantum Computing and Engineering, 2022
Privacy-Preserving and Robust Federated Deep Metric Learning.
Proceedings of the 30th IEEE/ACM International Symposium on Quality of Service, 2022
Defenses Against Byzantine Attacks in Distributed Deep Neural Networks.
IEEE Trans. Netw. Sci. Eng., 2021
ToFi: An Algorithm to Defend Against Byzantine Attacks in Federated Learning.
Proceedings of the Security and Privacy in Communication Networks, 2021
Defending Against Byzantine Attacks in Quantum Federated Learning.
Proceedings of the 17th International Conference on Mobility, Sensing and Networking, 2021
Neuron Manifold Distillation for Edge Deep Learning.
Proceedings of the 29th IEEE/ACM International Symposium on Quality of Service, 2021
CE-SGD: Communication-Efficient Distributed Machine Learning.
Proceedings of the IEEE Global Communications Conference, 2021
A Survey of Virtual Machine Management in Edge Computing.
Proc. IEEE, 2019
FABA: An Algorithm for Fast Aggregation against Byzantine Attacks in Distributed Neural Networks.
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019
eSGD: Communication Efficient Distributed Deep Learning on the Edge.
Proceedings of the USENIX Workshop on Hot Topics in Edge Computing, 2018