Qi Xia

Orcid: 0000-0002-5096-3329

Affiliations:
  • College of William and Mary, Department of Computer Science, Williamsburg, VA, USA (PhD 2021)


According to our database1, Qi Xia authored at least 13 papers between 2019 and 2023.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

Online presence:

On csauthors.net:

Bibliography

2023
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

LAWS: Look Around and Warm-Start Natural Gradient Descent for Quantum Neural Networks.
Proceedings of the IEEE International Conference on Quantum Software, 2023

2021
A survey of federated learning for edge computing: Research problems and solutions.
High Confid. Comput., June, 2021

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

Efficient Privacy-Preserving Federated Learning for Resource-Constrained Edge Devices.
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

QuantumFed: A Federated Learning Framework for Collaborative Quantum Training.
Proceedings of the IEEE Global Communications Conference, 2021

CE-SGD: Communication-Efficient Distributed Machine Learning.
Proceedings of the IEEE Global Communications Conference, 2021

2019
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


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