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Hi, I'm Feng Zhu😄. I'm currently a second-year 👨🎓PhD student at 🏛️North Carolina State University.
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My research interests include Optimization, Federated Learning, and Theoretical (Multi-Agent) RL.
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Relevant courses📖 I've taken are Theoretical Foundations of Large-Scale Machine Learning and Optimization, Convex Optimization in Data Science, Real Analysis, Probability and Stochastic Processes, Linear Transformation & Matrix Theory, etc.
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I have achieved my Bachelor's and Master's degrees from Fudan University at 2016 and 2020, both in Engineering.
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Please feel free to contact me at fzhu5@ncsu.edu!
- Raleigh, NC
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15:02
(UTC -05:00) - https://sites.google.com/ncsu.edu/fengzhu/about
- in/feng-zhu-4738112a2
- https://scholar.google.com/citations?hl=en&user=ZqdH9HwAAAAJ
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Joined
Dec 18, 2024
Pinned Loading
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Fast-FedPG---Towards-Fast-Rates-for-Federated-and-Multi-Task-Reinforcement-Learning
Fast-FedPG---Towards-Fast-Rates-for-Federated-and-Multi-Task-Reinforcement-Learning PublicThis work is a conference paper published at IEEE CDC 2024. The paper is dedicated to finding a policy that maximizes the average of long-term cumulative rewards across environments. Included in th…
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STSyn---Speeding-Up-Local-SGD-with-Straggler-Tolerant-Synchronization
STSyn---Speeding-Up-Local-SGD-with-Straggler-Tolerant-Synchronization PublicThis work is a journal paper published at IEEE TSP in 2024, concentrating on improving the robustness to stragglers in distributed/federated learning with synchronous local SGD.
Python
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DRAG---Divergence-Based-Adaptive-Aggregation-in-Federated-Learning-on-Non-IID-Data
DRAG---Divergence-Based-Adaptive-Aggregation-in-Federated-Learning-on-Non-IID-Data PublicIn this work, we developed a novel algorithm named divergence-based adaptive aggregation (DRAG) to deal with the client-drift effect. Additionally, the DRAG algorithm also showcases resilience agai…
Python 1
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AgeSel---Communication-Efficient-Local-SGD-with-Age-Based-Worker-Selection
AgeSel---Communication-Efficient-Local-SGD-with-Age-Based-Worker-Selection PublicThis work is a journal paper published at The Journal of Supercomputing in 2023, focusing on improving communication efficiency of a distributed learning system using age-based worker selection tec…
Python
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G-CADA---Adaptive-Worker-grouping-for-Communication-Efficient-and-Straggler-Tolerant-Distributed-SGD
G-CADA---Adaptive-Worker-grouping-for-Communication-Efficient-and-Straggler-Tolerant-Distributed-SGD PublicThis work was published at IEEE ISIT 2022, where we proposed a novel algorithm named G-CADA aiming to improve the time and communication efficiency of distributed learning systems based on grouping…
Python
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