DACFL: Dynamic Average Consensus Based Federated Learning in Decentralized Topology

no code implementations • 10 Nov 2021 • Zhikun Chen, Daofeng Li, Jinkang Zhu, Sihai Zhang

Federated learning (FL) is a burgeoning distributed machine learning framework where a central parameter server (PS) coordinates many local users to train a globally consistent model.

Federated Learning

Resonant Beam Communications with Echo Interference Elimination

no code implementations • 25 Jun 2021 • Mingliang Xiong, Qingwen Liu, Gang Wang, Georgios B. Giannakis, Sihai Zhang, Jinkang Zhu, Chuan Huang

Resonant beam communications (RBCom) is capable of providing wide bandwidth when using light as the carrier.

Knowledge-Driven Machine Learning: Concept, Model and Case Study on Channel Estimation

no code implementations • 21 Dec 2020 • Daofeng Li, Kaihe Deng, Ming Zhao, Sihai Zhang, Jinkang Zhu

The power of big data and machine learning has been drastically demonstrated in many fields during the past twenty years which somehow leads to the vague even false understanding that the huge amount of precious human knowledge accumulated to date no longer seems to matter.

BIG-bench Machine Learning

Semi-Federated Learning

1 code implementation • 28 Mar 2020 • Zhikun Chen, Daofeng Li, Ming Zhao, Sihai Zhang, Jinkang Zhu

Federated learning (FL) enables massive distributed Information and Communication Technology (ICT) devices to learn a global consensus model without any participants revealing their own data to the central server.

Clustering Federated Learning