The University of Glasgow uses cookies for analytics. Find out more about our Privacy policy.
Necessary cookies enable core functionality. The website cannot function properly without these cookies, and can only be disabled by changing your browser preferences.
Analytical cookies help us improve our website. We use Google Analytics. All data is anonymised.
Clarity helps us to understand our users’ behaviour by visually representing their clicks, taps and scrolling. All data is anonymised.
q.long.1@research.gla.ac.uk
Research title: Collaborative Distributed Machine Learning: From Knowledge Reuse to Sparsification in Federated Learning
My research interests are as follows: Distributed Computing, Machine/Statistical Learning, Edge/Fog Computing.
My research will be conducted in collaboration with the Essence Lab, IDA Section, School of Computing Science.
Long, Q. , Anagnostopoulos, C. and Kolomvatsos, K. (2024) Enhancing knowledge reusability: a distributed multitask machine learning approach. IEEE Transactions on Emerging Topics in Computing, (doi: 10.1109/TETC.2024.3390811) (Early Online Publication)
Long, Q. , Anagnostopoulos, C. , Puthiya, S. and Bi, D. (2024) FedDIP: Federated Learning with Extreme Dynamic Pruning and Incremental Regularization. In: IEEE ICDM 2023, Shanghai, China, 1-4 December 2023, pp. 1187-1192. ISBN 9798350307887 (doi: 10.1109/ICDM58522.2023.00146)
Long, Q. , Kolomvatsos, K. and Anagnostopoulos, C. (2022) Knowledge reuse in edge computing environments. Journal of Network and Computer Applications, 206, 103466. (doi: 10.1016/j.jnca.2022.103466)
Long, Q. , Anagnostopoulos, C. and Kolomvatsos, K. (2024) Enhancing knowledge reusability: a distributed multitask machine learning approach. IEEE Transactions on Emerging Topics in Computing, (doi: 10.1109/TETC.2024.3390811) (Early Online Publication)
Long, Q. , Kolomvatsos, K. and Anagnostopoulos, C. (2022) Knowledge reuse in edge computing environments. Journal of Network and Computer Applications, 206, 103466. (doi: 10.1016/j.jnca.2022.103466)
Long, Q. , Anagnostopoulos, C. , Puthiya, S. and Bi, D. (2024) FedDIP: Federated Learning with Extreme Dynamic Pruning and Incremental Regularization. In: IEEE ICDM 2023, Shanghai, China, 1-4 December 2023, pp. 1187-1192. ISBN 9798350307887 (doi: 10.1109/ICDM58522.2023.00146)