434 citations · 490 across the 12 of their papers we have counts for
3 papers · 1 filter
Distilling On-Device Intelligence at the Network Edge
Jihong Park, Shiqiang Wang, Anis Elgabli +6
Devices at the edge of wireless networks are the last mile data sources for machine learning (ML). As opposed to traditional ready-made public datasets, these user-generated privat…
Multi-hop Federated Private Data Augmentation with Sample Compression
Eunjeong Jeong, Seungeun Oh, Jihong Park +3
On-device machine learning (ML) has brought about the accessibility to a tremendous amount of data from the users while keeping their local data private instead of storing it in a…
Federated Reinforcement Distillation with Proxy Experience Memory
Han Cha, Jihong Park, Hyesung Kim +2
In distributed reinforcement learning, it is common to exchange the experience memory of each agent and thereby collectively train their local models. The experience memory, howeve…