no code implementations • 15 Apr 2024 • Ming Xiang, Stratis Ioannidis, Edmund Yeh, Carlee Joe-Wong, Lili Su
It consists of a parameter server and a possibly large collection of clients (e. g., in cross-device federated learning) that may operate in congested and changing environments.
no code implementations • 1 Jun 2023 • Ming Xiang, Stratis Ioannidis, Edmund Yeh, Carlee Joe-Wong, Lili Su
Specifically, in each round $t$, the link between the PS and client $i$ is active with probability $p_i^t$, which is $\textit{unknown}$ to both the PS and the clients.
no code implementations • 31 May 2023 • Lili Su, Ming Xiang, Jiaming Xu, Pengkun Yang
Federated learning is a decentralized machine learning framework that enables collaborative model training without revealing raw data.
no code implementations • 3 Oct 2022 • Ming Xiang, Lili Su
Federated Learning (FL) is a nascent decentralized learning framework under which a massive collection of heterogeneous clients collaboratively train a model without revealing their local data.
1 code implementation • 25 May 2017 • Yihui He, Ming Xiang
We evaluate greedy, 2-opt, and genetic algorithms.