121 citations · 431 across the 34 of their papers we have counts for
7 papers · 1 filter
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…
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…
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…
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…