On the Tradeoff between Energy, Precision, and Accuracy in Federated Quantized Neural Networks. - Minsu Kim 003; Walid Saad; Mohammad Mozaffari; Mérouane Debbah (ICC, 2022)

Ensuring Data Freshness for Blockchain-Enabled Monitoring Networks. - Minsu Kim 003; Sungho Lee; Chanwon Park; Jemin Lee; Walid Saad (IEEE Internet Things J., 2022)

A Bargaining Game for Personalized, Energy Efficient Split Learning over Wireless Networks. - Minsu Kim 003; Alexander C. DeRieux; Walid Saad (WCNC, 2023)

Facing to Latency of Hyperledger Fabric for Blockchain-Enabled IoT: Modeling and Analysis. - Sungho Lee; Minsu Kim 003; Jemin Lee; Ruei-Hau Hsu; Min-Soo Kim 002; Tony Q. S. Quek (IEEE Netw., 2023)

Analysis of the Memorization and Generalization Capabilities of AI Agents: are Continual Learners Robust? - Minsu Kim 003; Walid Saad (ICASSP, 2024)

SpaFL: Communication-Efficient Federated Learning With Sparse Models And Low Computational Overhead. - Minsu Kim 003; Walid Saad; Mérouane Debbah; Choong Seon Hong (NeurIPS, 2024)

Green, Quantized Federated Learning Over Wireless Networks: An Energy-Efficient Design. - Minsu Kim 003; Walid Saad; Mohammad Mozaffari; Mérouane Debbah (IEEE Trans. Wirel. Commun., 2024)

Edge vs Cloud: How Do We Balance Cost, Latency, and Quality for Large Language Models Over 5G Networks? - Minsu Kim 003; Pinyarash Pinyoanuntapong; Bong-Ho Kim; Walid Saad; Doru Calin (WCNC, 2025)