Elastically-Constrained Meta-Learner for Federated Learning

no code implementations • 29 Jun 2023 • Peng Lan, Donglai Chen, Chong Xie, Keshu Chen, Jinyuan He, Juntao Zhang, Yonghong Chen, Yan Xu

One of the challenges in federated learning is non-IID data between clients, as a single model can not fit the data distribution for all clients.

Federated Learning Meta-Learning

Walle: An End-to-End, General-Purpose, and Large-Scale Production System for Device-Cloud Collaborative Machine Learning

no code implementations • 30 May 2022 • Chengfei Lv, Chaoyue Niu, Renjie Gu, Xiaotang Jiang, Zhaode Wang, Bin Liu, Ziqi Wu, Qiulin Yao, Congyu Huang, Panos Huang, Tao Huang, Hui Shu, Jinde Song, Bin Zou, Peng Lan, Guohuan Xu, Fei Wu, Shaojie Tang, Fan Wu, Guihai Chen

Walle consists of a deployment platform, distributing ML tasks to billion-scale devices in time; a data pipeline, efficiently preparing task input; and a compute container, providing a cross-platform and high-performance execution environment, while facilitating daily task iteration.