| Accelerated Federated Learning via Greedy Aggregation Y Lee, S Park, JH Ahn, J Kang IEEE Communications Letters 26 (12), 2919-2923, 2022 | 5 | 2022 |
| Security-Preserving Federated Learning via Byzantine-Sensitive Triplet Distance Y Lee, S Park, J Kang arXiv preprint arXiv:2210.16519, 2022 | 3 | 2022 |
| Fast-Convergent Federated Learning via Cyclic Aggregation Y Lee, S Park, J Kang 2023 IEEE International Conference on Image Processing (ICIP), 2175-2179, 2023 | 2 | 2023 |
| Federated AI for Defense Applications Y Lee, T Park, C Park, J Seo Journal of Defense and Security 5 (2), 206-222, 2023 | | 2023 |
| Byzantine-Resilient Federated Learning via Reverse Aggregation Y Lee, S Park, J Kang 2023 IEEE Virtual Conference on Communications (VCC), 98-102, 2023 | | 2023 |
| Neural Network Training Method and Apparatus Using Federated Learning Y Lee, S Park, J Kang KR Patent 10-2,479,793, 2022 | | 2022 |
| Federated Learning Method and System Y Lee, J Kang KR Patent 10-2,390,553, 2022 | | 2022 |
| Method and System for Byzantine-Resilient and Personalized Distributed Learning Y Lee, S Park, J Kang KR Patent 10-2,522,053, 2022 | | 2022 |
| FedLN: Federated Learning with Local Normalization Y Lee, J Kang The 31st Joint Conference on Communications and Information, 2021 | | 2021 |
Sangwoo ParkKing's College LondonVerified email at kcl.ac.uk
