Search Results for author: Jaemin Shin

Found 4 papers, 2 papers with code

FedBalancer: Data and Pace Control for Efficient Federated Learning on Heterogeneous Clients

1 code implementation5 Jan 2022 Jaemin Shin, Yuanchun Li, Yunxin Liu, Sung-Ju Lee

Federated Learning (FL) trains a machine learning model on distributed clients without exposing individual data.

Federated Learning

Flame: Simplifying Topology Extension in Federated Learning

1 code implementation9 May 2023 Harshit Daga, Jaemin Shin, Dhruv Garg, Ada Gavrilovska, Myungjin Lee, Ramana Rao Kompella

We present Flame, a new system that provides flexibility of the topology configuration of distributed FL applications around the specifics of a particular deployment context, and is easily extensible to support new FL architectures.

Federated Learning

Not All Federated Learning Algorithms Are Created Equal: A Performance Evaluation Study

no code implementations26 Mar 2024 Gustav A. Baumgart, Jaemin Shin, Ali Payani, Myungjin Lee, Ramana Rao Kompella

(3) However, algorithms such as FedDyn and SCAFFOLD are more prone to catastrophic failures without the support of additional techniques such as gradient clipping.

Federated Learning

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