Jinhyun So

Jinhyun So

Assistant Professor @ DGIST

Publications

You can also find my articles on my Google Scholar profile.

Pre-prints

  1. Jinhyun So, Kevin Hsieh, Behnaz Arzani, Shadi Noghabi, Salman Avestimehr, Ranveer Chandra, “FedSpace: An Efficient Federated Learning Framework at Satellites and Ground Stations,” arXiv preprint arXiv:2202.01267.

Journal Papers

  1. Jinhyun So, Basak Guler, and Salman Avestimehr, “Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learning,” IEEE Journal on Selected Areas in Information Theory 2.1 (2021): 479-489.
  2. Jinhyun So*, Basak Guler*, Salman Avestimehr, “ CodedPivateML: A Fast and Privacy-Preserving Framework for Distributed Machine Learning,” IEEE Journal on Selected Areas in Information Theory 2.1 (2021): 441-451.
  3. Jinhyun So, Basak Guler, and Salman Avestimehr, “Byzantine-Resilient Secure Federated Learning,” IEEE Journal on Selected Areas in Communications (JSAC) 2020.

Conference Papers

  1. J. So*, H. Kwon, “Universal Auto-encoder Framework for MIMO CSI Feedback” in IEEE Global Communications Conference (Globecom), Dec, 2023.
  2. J. So, R. E. Ali, B. Guler, J Jiao, A.S. Avestimehr, “Securing Secure Aggregation: Mitigating Multi-Round Privacy Leakage in Federated Learning,” in The 37th AAAI Conference on Artificial Intelligence, Feb, 2023.
  3. Baturalp Buyukates, Jinhyun So, Hessam Mahdavifar, A. Salman Avestimehr, “LightVeriFL: a Lightweight and Verifiable Secure Federated Learning,” in International Workshop on Federated Learning: Recent Advances and New Challenges in Conjunction with NeurIPS 2022 (FL-NeurIPS’22), Dec, 2022 (oral presentation).
  4. J. So*, C. He* , C Yang*, S. Li, Q. Yu, R. E. Ali, B. Guler, A.S. Avestimehr, “LightSecAgg: a Lightweight and Versatile Design for Secure Aggregation in Federated Learning” in the fifth Conference on Machine Learning Systems (MLsys), 2022 (oral presentation).
  5. J. So, R. E. Ali, B. Guler, J Jiao, A.S. Avestimehr, “Securing Secure Aggregation: Mitigating Multi-Round Privacy Leakage in Federated Learning,” in International Workshop on Trustable, Verifiable and Auditable Federated Learning in Conjunction with AAAI (FL-AAAI-22), Mar. 2022. [Presentation]
  6. J. So*, R. E. Ali, B. Guler, A.S. Avestimehr, “Secure Aggregation for Buffered Asynchronous Federated Learning,” in Workshop on New Frontiers in Federated Learning in Conjunction with NeurIPS, Dec. 2021.
  7. C Yang, J. So, C. He, S. Li, Q. Yu, and S. Avestimehr, “LightSecAgg: Rethinking Secure Aggregation in Federated Learning,’’ in IEEE Information Theory Workshop (ITW), Dec. 2021.
  8. Ramy E. Ali, Jinhyun So, and Salman Avestimehr. “On Polynomial Approximations for Privacy-Preserving and Verifiable ReLU Networks,” in Workshop on Privacy-preserving Machine Learning in Conjunction with NeurIPS, Dec. 2021. available online at arXiv:2011.05530. [Presentation]
  9. Chaoyang He, Songze Li, Jinhyun So, Mi Zhang, Hongyi Wang, Xiaoyang Wang, Praneeth Vepakomma et al. “Fedml: A research library and benchmark for federated machine learning.” in NeurIPS 2020 SpicyFL Workshop (Bast Paper Award), available online at arXiv:2007.13518. [Presentation] [Slides]
  10. Jinhyun So, Basak Guler, and Salman Avestimehr, “A Scalable Approach for Privacy-Preserving Collaborative Machine Learning,” in the 34th Conference on Neural Information Processing Systems (NeurIPS), Dec. 2020. [Presentation]
  11. Jinhyun So, Basak Guler, and Salman Avestimehr, “Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learning,” in ICML Workshop on Federated Learning for User Privacy and Data Confidentiality (Long Presentation), Jul. 2020. [Presentation]
  12. Jinhyun So, Basak Guler, Salman Avestimehr, and Payman Mohassel, “CodedPrivateML: A Fast and Privacy-Preserving Framework for Distributed Machine Learning,” in International Workshop on Coding Theory For Large-scale Machine Learning (CodML) in Conjunction with ICML, Jun. 2019.
  13. Jinhyun So, Sungho Choi, Seung Hyeok Ahn, Eui-Rim Jeong, and Yong H. Lee, “Digital Predistortion Based on Envelope Feedback,” in IEEE International Conference on Acoustics, Speech, and Signal Processing(ICASSP), Mar. 2012.
  14. Sungho Choi, Eui-Rim Jeong, Byonghwan Lee, Jinhyun So, and Yong H. Lee, “New Predistortion Technique for Wideband Power Amplifiers of Dual-Band Transmission Systems,” The 21st Joint Conference on Communications & Information 2011, May. 2011.

* : equal contribution

Patents

  1. Jinhyun So, Seokjoong Heo, Soobok Yeo, Sejin Kong, Jaehak Lee, and Mingu Kim, “Communication device and control method thereof,” Pending, U.S. Patent (US9712345), and Korea Patent (No.2015-0151308).
  2. Seokjoong Heo, Jinhyun So, Soobok Yeo, and Mingu Kim, “Receiver apparatus and reception method in wireless communication system,” Grant, U.S. Patent (US9479360), Korea Patent(10-0118975).
  3. Jinhyun So, Sungho Choi, Seung Hyeok Ahn, Eui-Rim Jeong, and Yong H. Lee, “Low-cost digital predistortion apparatus and method using envelope detection feedback,” Grant, U.S. Patent (US9148093), Korea Patent (10-1389880).
  4. Sungho Choi, Seung Hyeok Ahn, Eui-Rim Jeong, Jinhyun So, and Yong H. Lee, “Method and Appratus for pre-compensation of self-local oscillator coupling effect in transmitters,” Grant, Korea Patent (10-1265241)