1.2k citations · 9.5k across the 261 of their papers we have counts for
10 papers · 2 filters
Vehicular Cooperative Perception Through Action Branching and Federated Reinforcement Learning
Mohamed K. Abdel-Aziz, Cristina Perfecto, Sumudu Samarakoon +2
Cooperative perception plays a vital role in extending a vehicle's sensing range beyond its line-of-sight. However, exchanging raw sensory data under limited communication resource…
Federated Knowledge Distillation
Hyowoon Seo, Jihong Park, Seungeun Oh +2
Distributed learning frameworks often rely on exchanging model parameters across workers, instead of revealing their raw data. A prime example is federated learning that exchanges…
BayGo: Joint Bayesian Learning and Information-Aware Graph Optimization
Tamara Alshammari, Sumudu Samarakoon, Anis Elgabli +1
This article deals with the problem of distributed machine learning, in which agents update their models based on their local datasets, and aggregate the updated models collaborati…
Communication Efficient Distributed Learning with Censored, Quantized, and Generalized Group ADMM
Chaouki Ben Issaid, Anis Elgabli, Jihong Park +2
In this paper, we propose a communication-efficiently decentralized machine learning framework that solves a consensus optimization problem defined over a network of inter-connecte…
Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applications
Jihong Park, Sumudu Samarakoon, Anis Elgabli +4
Machine learning (ML) is a promising enabler for the fifth generation (5G) communication systems and beyond. By imbuing intelligence into the network edge, edge nodes can proactive…
Harnessing Wireless Channels for Scalable and Privacy-Preserving Federated Learning
Anis Elgabli, Jihong Park, Chaouki Ben Issaid +1
Wireless connectivity is instrumental in enabling scalable federated learning (FL), yet wireless channels bring challenges for model training, in which channel randomness perturbs…
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…
XOR Mixup: Privacy-Preserving Data Augmentation for One-Shot Federated Learning
MyungJae Shin, Chihoon Hwang, Joongheon Kim +3
User-generated data distributions are often imbalanced across devices and labels, hampering the performance of federated learning (FL). To remedy to this non-independent and identi…
Proxy Experience Replay: Federated Distillation for Distributed Reinforcement Learning
Han Cha, Jihong Park, Hyesung Kim +2
Traditional distributed deep reinforcement learning (RL) commonly relies on exchanging the experience replay memory (RM) of each agent. Since the RM contains all state observations…
Federated Learning in the Sky: Joint Power Allocation and Scheduling with UAV Swarms
Tengchan Zeng, Omid Semiari, Mohammad Mozaffari +3
Unmanned aerial vehicle (UAV) swarms must exploit machine learning (ML) in order to execute various tasks ranging from coordinated trajectory planning to cooperative target recogni…