Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data. - Eunjeong Jeong; Seungeun Oh; Hyesung Kim; Jihong Park; Mehdi Bennis; Seong-Lyun Kim (CoRR, 2018)

Multi-hop Federated Private Data Augmentation with Sample Compression. - Eunjeong Jeong; Seungeun Oh; Jihong Park; Hyesung Kim; Mehdi Bennis; Seong-Lyun Kim (CoRR, 2019)

Distilling On-Device Intelligence at the Network Edge. - Jihong Park; Shiqiang Wang; Anis Elgabli; Seungeun Oh; Eunjeong Jeong; Han Cha; Hyesung Kim; Seong-Lyun Kim; Mehdi Bennis (CoRR, 2019)

Mix2FLD: Downlink Federated Learning After Uplink Federated Distillation With Two-Way Mixup. - Seungeun Oh; Jihong Park; Eunjeong Jeong; Hyesung Kim; Mehdi Bennis; Seong-Lyun Kim (IEEE Commun. Lett., 2020)

Hiding in the Crowd: Federated Data Augmentation for On-Device Learning. - Eunjeong Jeong; Seungeun Oh; Jihong Park; Hyesung Kim; Mehdi Bennis; Seong-Lyun Kim (IEEE Intell. Syst., 2021)

Asynchronous Decentralized Learning over Unreliable Wireless Networks. - Eunjeong Jeong; Matteo Zecchin; Marios Kountouris (ICC, 2022)

Personalized Decentralized Federated Learning with Knowledge Distillation. - Eunjeong Jeong; Marios Kountouris (ICC, 2023)

DRACO: Decentralized Asynchronous Federated Learning over Continuous Row-Stochastic Network Matrices. - Eunjeong Jeong; Marios Kountouris (CoRR, 2024)

DRACO: Decentralized Asynchronous Federated Learning Over Row-Stochastic Wireless Networks. - Eunjeong Jeong; Marios Kountouris (IEEE Open J. Commun. Soc., 2025)

Battery-aware Cyclic Scheduling in Energy-harvesting Federated Learning. - Eunjeong Jeong; Nikolaos Pappas 001 (CoRR, 2025)