434 citations · 486 across the 7 of their papers we have counts for
8 papers
Trustworthy, Explainable, and Sustainable Decentralized Intelligence for 6G Networks
Giovanni Perin, Michele Rossi, Enrique Tomás Martínez Beltrán +13
As 6G networks transition from theoretical frameworks into operational realities, artificial intelligence (AI) evolves from an add-on optimization tool into a distributed and inter…
DRACO: Decentralized Asynchronous Federated Learning over Row-Stochastic Wireless Networks
Eunjeong Jeong, Marios Kountouris
Recent developments and emerging use cases, such as smart Internet of Things (IoT) and Edge AI, have sparked considerable interest in the training of neural networks over fully dec…
Personalized Decentralized Federated Learning with Knowledge Distillation
Eunjeong Jeong, Marios Kountouris
Personalization in federated learning (FL) functions as a coordinator for clients with high variance in data or behavior. Ensuring the convergence of these clients' models relies o…
Asynchronous Decentralized Learning over Unreliable Wireless Networks
Eunjeong Jeong, Matteo Zecchin, Marios Kountouris
Decentralized learning enables edge users to collaboratively train models by exchanging information via device-to-device communication, yet prior works have been limited to wireles…
Mix2FLD: Downlink Federated Learning After Uplink Federated Distillation With Two-Way Mixup
Seungeun Oh, Jihong Park, Eunjeong Jeong +3
This letter proposes a novel communication-efficient and privacy-preserving distributed machine learning framework, coined Mix2FLD. To address uplink-downlink capacity asymmetry, l…
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…
Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data
Eunjeong Jeong, Seungeun Oh, Hyesung Kim +3
On-device machine learning (ML) enables the training process to exploit a massive amount of user-generated private data samples. To enjoy this benefit, inter-device communication o…