Tianru Zhang
Postdoktor vid Institutionen för informationsteknologi; Systemteknik
- Telefon:
- 018-471 61 68
- E-post:
- tianru.zhang@it.uu.se
- Besöksadress:
- Hus 10, Regementsvägen 10
- Postadress:
- Box 337
751 05 UPPSALA
- ORCID:
- 0000-0001-9983-3755
Nyckelord
- Data management
- Multimodel AI
- Federated Learning
Biografi
Dr. Tianru Zhang is a Postdoctoral Researcher in Department of Information Technology at Uppsala University. He holds a PhD in scientific computing from Uppsala University. Previously, he received his MSc degree in Statistics and Data Science from ENSAI (National school of statistics and information analysis of France), and his BSc degree in Mathematics from University of Science and Technology of China (USTC). Before joining Uppsala University, he also worked as an assistant researcher in Fujitsu R&D center, Beijing. Zhang is interested in research field including Data Management, Federated Machine Learning, and Multimodel LLMs.
Publikationer
Urval av publikationer
-
Intelligent Data Management via Machine Learning: From Storage Hierarchy to Information Hierarchy
2025
-
Accelerating Fair Federated Learning: Adaptive Federated Adam
Ingår i IEEE Transactions on Machine Learning in Communications and Networking, s. 1017-1032, 2024
- DOI för Accelerating Fair Federated Learning: Adaptive Federated Adam
- Ladda ner fulltext (pdf) av Accelerating Fair Federated Learning: Adaptive Federated Adam
-
Autonomous Hierarchical Storage Management via Reinforcement Learning
2024
-
Efficient Hierarchical Storage Management Empowered by Reinforcement Learning
Ingår i IEEE Transactions on Knowledge and Data Engineering, s. 5780-5793, 2023
Senaste publikationer
-
Intelligent Data Management via Machine Learning: From Storage Hierarchy to Information Hierarchy
2025
-
Accelerating Fair Federated Learning: Adaptive Federated Adam
Ingår i IEEE Transactions on Machine Learning in Communications and Networking, s. 1017-1032, 2024
- DOI för Accelerating Fair Federated Learning: Adaptive Federated Adam
- Ladda ner fulltext (pdf) av Accelerating Fair Federated Learning: Adaptive Federated Adam
-
Blades: A Unified Benchmark Suite for Byzantine Attacks and Defenses in Federated Learning
Ingår i 2024 IEEE/ACM Ninth International Conference on Internet-of-Things Design and Implementation (IoTDI), s. 158-169, 2024
-
Autonomous Hierarchical Storage Management via Reinforcement Learning
2024
-
Ingår i Expert systems with applications, 2024
- DOI för Data management of scientific applications in a reinforcement learning-based hierarchical storage system
- Ladda ner fulltext (pdf) av Data management of scientific applications in a reinforcement learning-based hierarchical storage system
Alla publikationer
Artiklar i tidskrift
-
Accelerating Fair Federated Learning: Adaptive Federated Adam
Ingår i IEEE Transactions on Machine Learning in Communications and Networking, s. 1017-1032, 2024
- DOI för Accelerating Fair Federated Learning: Adaptive Federated Adam
- Ladda ner fulltext (pdf) av Accelerating Fair Federated Learning: Adaptive Federated Adam
-
Ingår i Expert systems with applications, 2024
- DOI för Data management of scientific applications in a reinforcement learning-based hierarchical storage system
- Ladda ner fulltext (pdf) av Data management of scientific applications in a reinforcement learning-based hierarchical storage system
-
Efficient Hierarchical Storage Management Empowered by Reinforcement Learning
Ingår i IEEE Transactions on Knowledge and Data Engineering, s. 5780-5793, 2023
Doktorsavhandlingar, sammanläggning
Konferensbidrag
-
Blades: A Unified Benchmark Suite for Byzantine Attacks and Defenses in Federated Learning
Ingår i 2024 IEEE/ACM Ninth International Conference on Internet-of-Things Design and Implementation (IoTDI), s. 158-169, 2024
-
Autonomous Hierarchical Storage Management via Reinforcement Learning
2024
-
Demo Abstract: Blades: A Unified Benchmark Suite for Byzantine-Resilient in Federated Learning
Ingår i 9TH ACM/IEEE CONFERENCE ON INTERNET OF THINGS DESIGN AND IMPLEMENTATION, IOTDI 2024, s. 229-230, 2024
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Efficient Hierarchical Storage Management Empowered by Reinforcement Learning Extended Abstract
s. 3869-3870, 2023