121 citations · 431 across the 34 of their papers we have counts for
34 papers
NeFL: Nested Model Scaling for Federated Learning with System Heterogeneous Clients
Honggu Kang, Seohyeon Cha, Jinwoo Shin +2
Federated learning (FL) enables distributed training while preserving data privacy, but stragglers-slow or incapable clients-can significantly slow down the total training time and…
Energy-Efficient Vehicular Edge Computing with One-by-one Access Scheme
Youngsu Jang, Seongah Jeong, Joonhyuk Kang
With the advent of ever-growing vehicular applications, vehicular edge computing (VEC) has been a promising solution to augment the computing capacity of future smart vehicles. The…
Fast-Convergent Federated Learning via Cyclic Aggregation
Youngjoon Lee, Sangwoo Park, Joonhyuk Kang
Federated learning (FL) aims at optimizing a shared global model over multiple edge devices without transmitting (private) data to the central server. While it is theoretically wel…
Security-Preserving Federated Learning via Byzantine-Sensitive Triplet Distance
Youngjoon Lee, Sangwoo Park, Joonhyuk Kang
While being an effective framework of learning a shared model across multiple edge devices, federated learning (FL) is generally vulnerable to Byzantine attacks from adversarial ed…
Compressed Particle-Based Federated Bayesian Learning and Unlearning
Jinu Gong, Osvaldo Simeone, Joonhyuk Kang
Conventional frequentist FL schemes are known to yield overconfident decisions. Bayesian FL addresses this issue by allowing agents to process and exchange uncertainty information…
Hybrid UAV-enabled Secure Offloading via Deep Reinforcement Learning
Seonghoon Yoo, Seongah Jeong, Joonhyuk Kang
Unmanned aerial vehicles (UAVs) have been actively studied as moving cloudlets to provide application offloading opportunities and to enhance the security level of user equipments…
Forget-SVGD: Particle-Based Bayesian Federated Unlearning
Jinu Gong, Osvaldo Simeone, Rahif Kassab +1
Variational particle-based Bayesian learning methods have the advantage of not being limited by the bias affecting more conventional parametric techniques. This paper proposes to l…
Bayesian Variational Federated Learning and Unlearning in Decentralized Networks
Jinu Gong, Osvaldo Simeone, Joonhyuk Kang
Federated Bayesian learning offers a principled framework for the definition of collaborative training algorithms that are able to quantify epistemic uncertainty and to produce tru…
Meta-ViterbiNet: Online Meta-Learned Viterbi Equalization for Non-Stationary Channels
Tomer Raviv, Sangwoo Park, Nir Shlezinger +3
Deep neural networks (DNNs) based digital receivers can potentially operate in complex environments. However, the dynamic nature of communication channels implies that in some scen…
End-to-End Fast Training of Communication Links Without a Channel Model via Online Meta-Learning
Sangwoo Park, Osvaldo Simeone, Joonhyuk Kang
When a channel model is not available, the end-to-end training of encoder and decoder on a fading noisy channel generally requires the repeated use of the channel and of a feedback…
Cooperative Learning via Federated Distillation over Fading Channels
Jin-Hyun Ahn, Osvaldo Simeone, Joonhyuk Kang
Cooperative training methods for distributed machine learning are typically based on the exchange of local gradients or local model parameters. The latter approach is known as Fede…
From Learning to Meta-Learning: Reduced Training Overhead and Complexity for Communication Systems
Osvaldo Simeone, Sangwoo Park, Joonhyuk Kang
Machine learning methods adapt the parameters of a model, constrained to lie in a given model class, by using a fixed learning procedure based on data or active observations. Adapt…
Meta-Learning to Communicate: Fast End-to-End Training for Fading Channels
Sangwoo Park, Osvaldo Simeone, Joonhyuk Kang
When a channel model is available, learning how to communicate on fading noisy channels can be formulated as the (unsupervised) training of an autoencoder consisting of the cascade…
Energy-Efficient Task Offloading for Vehicular Edge Computing: Joint Optimization of Offloading and Bit Allocation
Youngsu Jang, Jinyeop Na, Seongah Jeong +1
With the rapid development of vehicular networks, various applications that require high computation resources have emerged. To efficiently execute these applications, vehicular ed…
Learning to Demodulate from Few Pilots via Offline and Online Meta-Learning
Sangwoo Park, Hyeryung Jang, Osvaldo Simeone +1
This paper considers an Internet-of-Things (IoT) scenario in which devices sporadically transmit short packets with few pilot symbols over a fading channel. Devices are characteriz…
Wireless Federated Distillation for Distributed Edge Learning with Heterogeneous Data
Jin-Hyun Ahn, Osvaldo Simeone, Joonhyuk Kang
Cooperative training methods for distributed machine learning typically assume noiseless and ideal communication channels. This work studies some of the opportunities and challenge…
Learning How to Demodulate from Few Pilots via Meta-Learning
Sangwoo Park, Hyeryung Jang, Osvaldo Simeone +1
Consider an Internet-of-Things (IoT) scenario in which devices transmit sporadically using short packets with few pilot symbols. Each device transmits over a fading channel and is…
Coded Federated Computing in Wireless Networks with Straggling Devices and Imperfect CSI
Sukjong Ha, Jingjing Zhang, Osvaldo Simeone +1
Distributed computing platforms typically assume the availability of reliable and dedicated connections among the processors. This work considers an alternative scenario, relevant…
Wireless Map-Reduce Distributed Computing with Full-Duplex Radios and Imperfect CSI
Sukjong Ha, Jingjing Zhang, Osvaldo Simeone +1
Consider a distributed computing system in which the worker nodes are connected over a shared wireless channel. Nodes can store a fraction of the data set over which computation ne…
On the Feasibility of Full-duplex Large-scale MIMO Cellular Systems
Jeongwan Koh, Yeon-Geun Lim, Chan-Byoung Chae +1
This paper concerns the feasibility of full-duplex large-scale multiple-input-multiple-output (MIMO) cellular systems. We first propose a pilot transmission scheme and assess its p…
Optimization of Massive Full-Dimensional MIMO for Positioning and Communication
Seongah Jeong, Osvaldo Simeone, Joonhyuk Kang
Massive Full-Dimensional multiple-input multiple-output (FD-MIMO) base stations (BSs) have the potential to bring multiplexing and coverage gains by means of three-dimensional (3D)…
Control-Data Separation with Decentralized Edge Control in Fog-Assisted Uplink Communications
Jinkyu Kang, Osvaldo Simeone, Joonhyuk Kang +1
Fog-aided network architectures for 5G systems encompass wireless edge nodes, referred to as remote radio systems (RRSs), as well as remote cloud center (RCC) processors, which are…
On the Trade-Off between Computational Load and Reliability for Network Function Virtualization
Jinkyu Kang, Osvaldo Simeone, Joonhyuk Kang
Network Function Virtualization (NFV) enables the "softwarization" of network functions, which are implemented on virtual machines hosted on Commercial off-the-shelf (COTS) servers…
Mobile Edge Computing via a UAV-Mounted Cloudlet: Optimization of Bit Allocation and Path Planning
Seongah Jeong, Osvaldo Simeone, Joonhyuk Kang
Unmanned Aerial Vehicles (UAVs) have been recently considered as means to provide enhanced coverage or relaying services to mobile users (MUs) in wireless systems with limited or n…