1 code implementation • 30 Mar 2024 • Heqiang Wang, Jieming Bian, Lei Wang
Moreover, we establish a convergence bound for our LVFL algorithm, which accounts for both communication and computational lightweighting ratios.
no code implementations • 18 Dec 2023 • Heqiang Wang, Jie Xu
However, deep learning-based CSS methods often rely on centralized learning, posing challenges like communication overhead and data privacy risks.
no code implementations • 28 Mar 2023 • Heqiang Wang, Jieming Bian, Jie Xu
In this study, we address the emerging field of Streaming Federated Learning (SFL) and propose local cache update rules to manage dynamic data distributions and limited cache capacity.
no code implementations • 26 May 2022 • Heqiang Wang, Jie Xu
Federated learning (FL) is a new distributed machine learning framework known for its benefits on data privacy and communication efficiency.
no code implementations • 10 Jan 2021 • Jie Xu, Heqiang Wang, Lixing Chen
For cooperative FL service providers, we design a distributed bandwidth allocation algorithm to optimize the overall performance of multiple FL services, meanwhile cater to the fairness among FL services and the privacy of clients.
no code implementations • 9 Apr 2020 • Jie Xu, Heqiang Wang
This paper studies federated learning (FL) in a classic wireless network, where learning clients share a common wireless link to a coordinating server to perform federated model training using their local data.