Výsledky vyhledávání - "Yih-Fang Huang"

Communication-Efficient Online Federated Learning Framework for Nonlinear Regression

Autor: Vinay Chakravarthi Gogineni, Stefan Werner, Yih-Fang Huang, Anthony Kuh

Publikováno v: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing

Federated learning (FL) literature typically assumes that each client has a fixed amount of data, which is unrealistic in many practical applications. Some recent works introduced a framework for online FL (Online-Fed) wherein clients perform model l

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3a1fce8c14b94dc4efd0ea56a1e3e12f
https://doi.org/10.1109/icassp43922.2022.9746228

Zobrazit plný text záznamu

Nonlinear Adaptive Filtering With Kernel Set-Membership Approach

Autor: Yih-Fang Huang, Anthony Kuh, Kewei Chen, Stefan Werner

Publikováno v: IEEE Transactions on Signal Processing. 68:1515-1528

This paper develops nonlinear kernel adaptive filtering algorithms based on the set-membership filtering (SMF) framework. The set-membership-based filtering approach is distinct from the conventional adaptive filtering approaches in that it aims for

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::2d9e48123be7490b62f92d386e65882b
https://doi.org/10.1109/tsp.2020.2975370

Zobrazit plný text záznamu

Decentralized Graph Federated Multitask Learning for Streaming Data

Autor: Vinay Chakravarthi Gogineni, Stefan Werner, Yih-Fang Huang, Anthony Kuh

In federated learning (FL), multiple clients connected to a single server train a global model based on locally stored data without revealing their data to the server or other clients. Nonetheless, the current FL architecture is highly vulnerable to

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::95bde29233bbcdd3740538112d9b4dcb
https://hdl.handle.net/11250/3058758

Zobrazit plný text záznamu

Resource-Aware Asynchronous Online Federated Learning for Nonlinear Regression

Autor: Francois Gauthier, Vinay Chakravarthi Gogineni, Stefan Werner, Yih-Fang Huang, Anthony Kuh

Many assumptions in the federated learning literature present a best-case scenario that can not be satisfied in most real-world applications. An asynchronous setting reflects the realistic environment in which federated learning methods must be able

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::68fb00328024f1ff759038e5966b026b
https://hdl.handle.net/11250/3058040

Zobrazit plný text záznamu

An Incentive Scheme for Sensor Fusion With Strategic Sensors

Autor: Kewei Chen, Yih-Fang Huang, Vijay Gupta, Donya Ghavidel Dobhakhshari

Publikováno v: IEEE Transactions on Signal Processing. 67:6342-6351

Distributed estimation that recruits potentially large groups of humans to collect data about a phenomenon of interest has emerged as a paradigm applicable to a broad range of detection and estimation tasks. However, it also presents a number of chal

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::89220299bf8768a7dd1ec1f5d8c58578
https://doi.org/10.1109/tsp.2019.2954974

Zobrazit plný text záznamu

On Stability and Convergence of Distributed Filters

Autor: Sayed Pouria Talebi, Stefan Werner, Yih-Fang Huang, Vijay Gupta

Publikováno v: IEEE Signal Processing Letters

Recent years have bore witness to the proliferation of distributed filtering techniques, where a collection of agents communicating over an ad-hoc network aim to collaboratively estimate and track the state of a system. These techniques form the enab

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3998b83693c39baf826f5eda4f590c82
https://hdl.handle.net/11250/2984233

Zobrazit plný text záznamu

Vyhledávací nástroje:

Upřesnit hledání