7 citations · 17 across the 12 of their papers we have counts for
13 papers
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…
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…
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…