1 code implementation • 7 Feb 2024 • Hailiang Li, Yan Huo, Yan Wang, Xu Yang, Miaohui Hao, Xiao Wang
As the modern CPU, GPU, and NPU chip design complexity and transistor counts keep increasing, and with the relentless shrinking of semiconductor technology nodes to nearly 1 nanometer, the placement and routing have gradually become the two most pivotal processes in modern very-large-scale-integrated (VLSI) circuit back-end design.
no code implementations • 29 Oct 2022 • Yue Wang, Zhi Tian, Xin Fan, Yan Huo, Cameron Nowzari, Kai Zeng
With the proliferation of versatile Internet of Things (IoT) services, smart IoT devices are increasingly deployed at the edge of wireless networks to perform collaborative machine learning tasks using locally collected data, giving rise to the edge learning paradigm.
no code implementations • 10 Aug 2022 • Xin Fan, Yue Wang, Yan Huo, Zhi Tian
data issues and Byzantine attacks, global data samples are introduced in CB-DSL and shared among IoT workers, which not only alleviates the local data heterogeneity effectively but also enables to fully utilize the exploration-exploitation mechanism of swarm intelligence.
no code implementations • 18 Oct 2021 • Xin Fan, Yue Wang, Yan Huo, Zhi Tian
As a promising distributed learning technology, analog aggregation based federated learning over the air (FLOA) provides high communication efficiency and privacy provisioning under the edge computing paradigm.
no code implementations • 8 Apr 2021 • Xin Fan, Yue Wang, Yan Huo, Zhi Tian
Federated learning (FL) is an attractive paradigm for making use of rich distributed data while protecting data privacy.
no code implementations • 30 Mar 2021 • Xin Fan, Yue Wang, Yan Huo, Zhi Tian
For distributed learning among collaborative users, this paper develops and analyzes a communication-efficient scheme for federated learning (FL) over the air, which incorporates 1-bit compressive sensing (CS) into analog aggregation transmissions.