no code implementations • 22 Apr 2024 • Marie Siew, Haoran Zhang, Jong-Ik Park, Yuezhou Liu, Yichen Ruan, Lili Su, Stratis Ioannidis, Edmund Yeh, Carlee Joe-Wong
We show how our fairness-based learning and incentive mechanisms impact training convergence and finally evaluate our algorithm with multiple sets of learning tasks on real world datasets.
no code implementations • 11 Dec 2021 • Yichen Ruan, Carlee Joe-Wong
Traditionally, clustered federated learning groups clients with the same data distribution into a cluster, so that every client is uniquely associated with one data distribution and helps train a model for this distribution.
no code implementations • 12 Jun 2020 • Yichen Ruan, Xiaoxi Zhang, Shu-Che Liang, Carlee Joe-Wong
Traditional federated learning algorithms impose strict requirements on the participation rates of devices, which limit the potential reach of federated learning.
no code implementations • 17 Apr 2020 • Yuwei Tu, Yichen Ruan, Su Wang, Satyavrat Wagle, Christopher G. Brinton, Carlee Joe-Wong
Unlike traditional federated learning frameworks, our method enables devices to offload their data processing tasks to each other, with these decisions determined through a convex data transfer optimization problem that trades off costs associated with devices processing, offloading, and discarding data points.
Distributed, Parallel, and Cluster Computing