Publications

Conference Papers

1. GEM: A Scale-Aware and Distribution-Sensitive Sparse Fine-Tuning Framework for Effective Downstream Adaptation [paper] [arXiv] 

Sungmin Kang, Jisoo Kim, Salman Avestimehr, Sunwoo Lee

AAAI, 2026

2. Layer-wise Update Aggregation with Recycling for Communication-Efficient Federated Learning [paper] [arXiv]

Jisoo Kim, Sungmin Kang, Sunwoo Lee

NeurIPS, 2025

3. Enabling Weak Client Participation via On-Device Knowledge Distillation in Heterogeneous Federated Learning [paper] [arXiv]

Jihyun Lim, Junhyuk Jo, Tuo Zhang, Sunwoo Lee

ECAI, 2025

4. Layer-Wise Adaptive Gradient Norm Penalizing Method for Efficient and Accurate Deep Learning [paper] [arXiv]

Sunwoo Lee

KDD, 2024

5. Layer-wise Adaptive Model Aggregation for Scalable Federated Learning (Oral Presentation) [paper] [arXiv]

Sunwoo Lee, Tuo Zhang, Salman Avestimehr

AAAI, 2023

6. FedAudio: A Federated Learning Benchmark for Audio Tasks [paper] [arXiv]

Tuo Zhang, Tiantian Feng, Samiul Alam, Sunwoo Lee, Mi Zhang, Shrikanth S. Narayanan, Salman Avestimehr

ICASSP, 2023

7. Using Multi-resolution Data to Accelerate Neural Network Training in Scientific Applications [paper]

Kewei Wang, Sunwoo Lee, Jan Balewski, Alex Sim, Peter Nugent, Ankit Agrawal, Alok Choudhary, Kesheng Wu, Wei-keng Liao

CCGrid, 2022

8. Supporting Data Compression in PnetCDF [paper]

Kaiyuan Hou, Qiao Kang, Sunwoo Lee, Ankit Agrawal, Alok Choudhary, Wei-keng Liao

BigData, 2021

9. Asynchronous I/O Strategy for Large-Scale Deep Learning Applications [paper]

Sunwoo Lee, Qiao Kang, Kewei Wang, Jan Balewski, Alex Sim, Ankit Agrawal, Alok Choudhary, Peter Nugent, Kesheng Wu, Wei-keng Liao

HiPC, 2021

10. SIGRNN: Synthetic minority Instances Generation in imbalanced datasets using a Recurrent Neural Network [paper]

Reda Al-Bahrani, Dipendra Jha, Qiao Kang, Sunwoo Lee, Zijiang Yang, Wei-keng Liao, Ankit Agrawal, Alok Choudhary

ICPRAM, 2021

11. Communication-Efficient Local Stochastic Gradient Descent for Scalable Deep Learning [paper]

Sunwoo Lee, Qiao Kang, Ankit Agrawal, Alok Choudhary, Wei-keng Liao

BigData, 2020

12. Improving All-to-Many Personalized Communication in Two-Phase I/O [paper]

Qiao Kang, Robert Ross, Robert Latham, Sunwoo Lee, Ankit Agrawal, Alok Choudhary, Wei-keng Liao

SC, 2020

13. Predicting Resource Requirement in Intermediate Palomar Transient Factory Workflow [paper]

Qiao Kang, Alex Sim, Peter Nugent, Sunwoo Lee, Wei-keng Liao, Ankit Agrawal, Alok Choudhary, Kesheng Wu

CCGrid, 2020

14. Improving Scalability of Parallel CNN Training by Adjusting Mini-Batch Size at Run-Time [paper]

Sunwoo Lee, Qiao Kang, Sandeep Madireddy, Prasanna Balaprakash, Ankit Agrawal, Alok Choudhary, Richard Archibald, Wei-keng Liao

BigData, 2019

15. Parallelizing Deep Convolutional Neural Network Training by Exploiting the Overlapping of Computation and Communication (Best Paper Finalist) [paper]

Sunwoo Lee, Dipendra Jha, Ankit Agrawal, Alok Choudhary, Wei-keng Liao

HiPC, 2017

16. Parallel Community Detection Algorithm Using a Data Partitioning Strategy with Pairwise Subdomain Duplication [paper]

Diana Palsetia, William Hendrix, Sunwoo Lee, Ankit Agrawal, Wei-keng Liao, Alok Choudhary

ISC, 2016

Journals

1. Highly Stable Two-level Current Fluctuation in Complex Oxide Heterostructures [paper]

Doyeop Kim, Jung-Woo Lee, Jihyun Lim, Sungjun Choi, Khimananda Acharya, Seobin Oh, Jaewhan Oh, Tula R. Paudel, Yongsoo Yang, Kitae Eom, Sunwoo Lee, Hyungwoo Lee

Nature Communications, 2025

2. Machine Learning Approach to Characterize Ferromagnetic La0.7Sr0.3MnO3 Thin Films via Featurization of Surface Morphology [paper]

Sanghyeok Ryou, Jihyun Lim, Minwoo Jang, Kitae Eom, Sunwoo Lee, Hyungwoo Lee

Advanced Science, 2025

3. Embracing Federated Learning: Enabling Weak Client Participation via Partial Model Training [paper]

Sunwoo Lee, Tuo Zhang, Saurav Prakash, Yue Niu, Salman Avestimehr

IEEE Transactions on Mobile Computing, 2024

4. Overcoming Resource Constraints in Federated Learning: Large Models Can Be Trained with only Weak Clients [paper] [arXiv]

Yue Niu, Saurav Prakash, Souvik Kundu, Sunwoo Lee, Salman Avestimehr

Transactions on Machine Learning Research, 2023

5. mL-BFGS: A Momentum-based L-BFGS for Distributed Large-Scale Neural Network Optimization [paper] [arXiv]

Yue Niu, Zalan Fabian, Sunwoo Lee, Mahdi Soltanolkotabi, Salman Avestimehr

Transactions on Machine Learning Research, 2023

6. Partial Model Averaging in Federated Learning: Performance Guarantees and Benefits [paper] [arXiv]

Sunwoo Lee, Anit Sahu, Chaoyang He, Salman Avestimehr

Neurocomputing, 2023

7. Achieving Small-Batch Accuracy with Large-Batch Scalability via Hessian-Aware Learning Rate Adjustment [paper]

Sunwoo Lee, Chaoyang He, Salman Avestimehr

Neural Networks, 2023

8. Probing Oxygen Vacancy Distribution in Oxide Heterostructure by Deep Learning-based Spectral Analysis of Current Noise [paper]

Sunwoo Lee, Jaeyoung Jeon, Hyungwoo Lee

Applied Surface Science, 2022

9. Variance-aware weight quantization of multi-level resistive switching devices based on Pt/LaAlO3/SrTiO3 heterostructures [paper]

Sunwoo Lee, Jaeyoung Jeon, Kitae Eom, Chaehwa Jeong, Yongsoo Yang, Ji-Yong Park, Chang-Beom Eom, Hyungwoo Lee

Scientific Reports, 2022

10. Improving Scalability of Parallel CNN Training by Adaptively Adjusting Parameter Update Frequency [paper]

Sunwoo Lee, Qiao Kang, Reda Al-Bahrani, Ankit Agrawal, Alok Choudhary, Wei-keng Liao

Journal of Parallel and Distributed Computing, 2022

11. A Case Study on Parallel HDF5 Dataset Concatenation for High-Energy Physics Data Analysis [paper] [arXiv]

Sunwoo Lee, Kai-yuan Hou, Kewei Wang, Saba Sehrish, Marc Paterno, James Kowalkowski, Quincey Koziol, Robert Ross, Ankit Agrawal, Alok Choudhary, Wei-keng Liao

Parallel Computing, 2022

12. In Situ Compression Artifact Removal in Scientific Data Using Deep Transfer Learning [paper]

Sandeep Madireddy, Ji Hwan Park, Sunwoo Lee, Prasanna Balaprakash, Shinjae Yoo, Wei-keng Liao, Cory D Hauck, M Paul Laiu, Richard Archibald

Machine Learning: Science and Technology, 2020

13. Improving MPI Collective I/O Performance with Intra-node Request Aggregation [paper] [arXiv]

Qiao Kang, Sunwoo Lee, Kai-yuan Hou, Robert Ross, Ankit Agrawal, Alok Choudhary, Wei-keng Liao

IEEE Transactions on Parallel and Distributed Systems, 2020

Workshop Papers

1. TimelyFL: Heterogeneity-aware Asynchronous Federated Learning with Adaptive Partial Training

Tuo Zhang, Lei Gao, Sunwoo Lee, Mi Zhang, Salman Avestimehr

CVPR workshop, 2025

2. Federated Learning of Large Model at the Edge via Principal Sub-Model Training

Yue Niu, Saurav Prakash, Souvik Kundu, Sunwoo Lee, Salman Avestimehr

NeurIPS workshop, 2022

3. Partial Model Averaging in Federated Learning: Performance Guarantees and Benefits

Sunwoo Lee, Anit Sahu, Chaoyang He, Salman Avestimehr

AAAI workshop, 2022

4. SSFL: Tackling Label Deficiency in Federated Learning via Personalized Self-Supervision (Best Paper Award)

Chaoyang He, Zhengyu Yang, Erum Mushtaq, Sunwoo Lee, Mahdi Soltanolkotabi, Salman Avestimehr

AAAI workshop, 2022

5. SLIM-QN: A Stochastic, Light, and Momentumized Quasi-Newton Optimizer for Deep Neural Networks

Yue Niu, Zalan Fabian, Sunwoo Lee, Mahdi Soltanolkotabi, Salman Avestimehr

ICML workshop, 2021

6. Communication-Efficient Parallelization Strategy for Deep Convolutional Neural Network Training

Sunwoo Lee, Ankit Agrawal, Prasanna Balaprakash, Alok Choudhary, Wei-keng Liao

SC workshop, 2018

Open Source S/W

• FedLUAR [GitHub Repo]: J. Kim et al., Layer-wise Update Aggregation with Recycling for Communication-efficient Federated Learning, NeurIPS, 2025.

• Federated Distillation [GitHub Repo]: J. Lim et al., Distillation-based Heterogeneous Federated Learning, ECAI, 2025.

• FedLAMA [GitHub Repo]: S.Lee et al., Layer-wise Adaptive Model Aggregation for Scalable Federated Learning, AAAI, 2023.