no code implementations • 16 Oct 2023 • Jingyang Zhu, Yuanming Shi, Yong Zhou, Chunxiao Jiang, Wei Chen, Khaled B. Letaief
We first provide a comprehensive study on the convergence of AirComp-based FedAvg (AirFedAvg) algorithms under both strongly convex and non-convex settings with constant and diminishing learning rates in the presence of data heterogeneity.
no code implementations • 3 Apr 2021 • Xizi Chen, Jingyang Zhu, Jingbo Jiang, Chi-Ying Tsui
Through permutation, the optimal arrangement of the weight matrix is obtained, and the sparse weight matrix is further compressed to a small and dense format to make full use of the hardware resources.
no code implementations • 30 Oct 2020 • Shuhao Xia, Jingyang Zhu, Yuhan Yang, Yong Zhou, Yuanming Shi, Wei Chen
In this paper, we consider federated learning (FL) over a noisy fading multiple access channel (MAC), where an edge server aggregates the local models transmitted by multiple end devices through over-the-air computation (AirComp).
no code implementations • 3 Nov 2017 • Jingyang Zhu, Jingbo Jiang, Xizi Chen, Chi-Ying Tsui
Furthermore, an energy-efficient hardware architecture, SparseNN, is proposed to exploit both the input and output sparsity.