Search Results for author: Kyi Thar

Found 5 papers, 1 papers with code

Attention on Personalized Clinical Decision Support System: Federated Learning Approach

no code implementations22 Jan 2024 Chu Myaet Thwal, Kyi Thar, Ye Lin Tun, Choong Seon Hong

Thus, our objective is to provide a personalized clinical decision support system with evolvable characteristics that can deliver accurate solutions and assist healthcare professionals in medical diagnosing.

Federated Learning

Federated Learning based Energy Demand Prediction with Clustered Aggregation

no code implementations28 Oct 2022 Ye Lin Tun, Kyi Thar, Chu Myaet Thwal, Choong Seon Hong

In this paper, we propose a recurrent neural network based energy demand predictor, trained with federated learning on clustered clients to take advantage of distributed data and speed up the convergence process.

energy management Federated Learning +1

Risk Adversarial Learning System for Connected and Autonomous Vehicle Charging

no code implementations2 Aug 2021 Md. Shirajum Munir, Ki Tae Kim, Kyi Thar, Dusit Niyato, Choong Seon Hong

To tackle this, we formulate an RDSS problem for the DSO, where the objective is to maximize the charging capacity utilization by satisfying the laxity risk of the DSO.

Autonomous Vehicles Scheduling

Edge-assisted Democratized Learning Towards Federated Analytics

no code implementations1 Dec 2020 Shashi Raj Pandey, Minh N. H. Nguyen, Tri Nguyen Dang, Nguyen H. Tran, Kyi Thar, Zhu Han, Choong Seon Hong

Therefore, we need to design a robust learning mechanism than the FL that (i) unleashes a viable infrastructure for FA and (ii) trains learning models with better generalization capability.

Distributed Computing Edge-computing +1

Distributed and Democratized Learning: Philosophy and Research Challenges

1 code implementation18 Mar 2020 Minh N. H. Nguyen, Shashi Raj Pandey, Kyi Thar, Nguyen H. Tran, Mingzhe Chen, Walid Saad, Choong Seon Hong

Consequently, many emerging cross-device AI applications will require a transition from traditional centralized learning systems towards large-scale distributed AI systems that can collaboratively perform multiple complex learning tasks.

Philosophy

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