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q.long.1@research.gla.ac.uk
Research title: Distributed Statistical Learning over Data Streams at the Network Edge
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, (Accepted for 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, (Accepted for 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)