Divyansh Jhunjhunwala

About Me

Hi! I am Divyansh, a third year PhD candidate in the Electrical and Computer Engineering department at Carnegie Mellon University, advised by Dr. Gauri Joshi. My research interests lie broadly in distributed optimization and machine learning, in particular federated learning.

In summer 22, I interned at IBM Research working with Dr. Shiqiang Wang on some interesting problems in federated learning.

Prior to CMU, I completed my Bachelors in Technology (B.Tech) in Electronics and Electrical Communication Engineering from IIT Kharagpur, where I received the Institute Silver Medal for graduating with the highest CGPA in my department.

Email  /  CV  /  Google Scholar

profile photo
Recent News

Oct 22: Our work on incentivizing clients for federated learning was accepted as an oral presentation at the FL-Neurips 22 workshop. (12% acceptance rate).

Aug 22: Completed my in-person internship at IBM T.J. Watson Research Center, New York.

April 22: Our team was selected as a finalist for the Qualcomm Innovation Fellowship for the research proposal "Incentivized Federated Learning for Data-Heterogeneous and Resource-Constrained Clients".

Research
inc fl image To Federate or Not To Federate: Incentivizing Client Participation in Federated Learning
Yae Jee Cho, Divyansh Jhunjhunwala , Tian Li, Virginia Smith, Gauri Joshi
Under review

Propose IncFL algorithm to explicitly maximize the fraction of clients that are incentivized to use the global model in federated learning.

fedvarp image FedVARP: Tackling the Variance Due to Partial Client Participation in Federated Learning
Divyansh Jhunjhunwala , Pranay Sharma, Aushim Nagarkatti, Gauri Joshi
Uncertainty in Artificial Intelligence (UAI), 2022

Propose FedVARP algorithm to deal with variance caused by only a few clients participating in every round of federated training.

spatial image Leveraging Spatial and Temporal Correlations in Sparsified Mean Estimation
Divyansh Jhunjhunwala , Ankur Mallick, Advait Gadhikar, Swanand Kadhe, Gauri Joshi
Advances in Neural Information Processing Systems (NeurIPS), 2021

Introduce notions of spatial and temporal correlations and show how they can be used to efficiently compute the mean of a set of vectors in a communication-limited setting.

adaquant fl image Adaptive Quantization of model updates for communication-efficient federated learning
Divyansh Jhunjhunwala , Advait Gadhikar, Gauri Joshi, Yonina C. Eldar
International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021

Propose an adaptive quantization strategy that aims to achieve communication efficiency as well as a low error floor by changing the number of quantization levels during training in federated learning.


Source code credit to Dr. Jon Barron.