Skip to content
View fzhu0628's full-sized avatar
😄
😄

Block or report fzhu0628

Block user

Prevent this user from interacting with your repositories and sending you notifications. Learn more about blocking users.

You must be logged in to block users.

Please don't include any personal information such as legal names or email addresses. Maximum 100 characters, markdown supported. This note will be visible to only you.
Report abuse

Contact GitHub support about this user’s behavior. Learn more about reporting abuse.

Report abuse
fzhu0628/README.md
  • Hi, I'm Feng Zhu😄. I'm currently a second-year 👨‍🎓PhD student at 🏛️North Carolina State University.

  • My research interests include Optimization, Federated Learning, and Theoretical (Multi-Agent) RL.

  • 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.

  • I have achieved my Bachelor's and Master's degrees from Fudan University at 2016 and 2020, both in Engineering.

  • Please feel free to contact me at fzhu5@ncsu.edu!

  • 🏛️Google scholar

  • 🌏Home page

  • 🔗LinkedIn

Pinned Loading

  1. Fast-FedPG---Towards-Fast-Rates-for-Federated-and-Multi-Task-Reinforcement-Learning Fast-FedPG---Towards-Fast-Rates-for-Federated-and-Multi-Task-Reinforcement-Learning Public

    This 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…

  2. STSyn---Speeding-Up-Local-SGD-with-Straggler-Tolerant-Synchronization STSyn---Speeding-Up-Local-SGD-with-Straggler-Tolerant-Synchronization Public

    This 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

  3. DRAG---Divergence-Based-Adaptive-Aggregation-in-Federated-Learning-on-Non-IID-Data DRAG---Divergence-Based-Adaptive-Aggregation-in-Federated-Learning-on-Non-IID-Data Public

    In 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

  4. AgeSel---Communication-Efficient-Local-SGD-with-Age-Based-Worker-Selection AgeSel---Communication-Efficient-Local-SGD-with-Age-Based-Worker-Selection Public

    This 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

  5. 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 Public

    This 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