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 • 14 Mar 2024 • Lei Wang, Jieming Bian, Letian Zhang, Chen Chen, Jie Xu
Federated learning (FL) allows collaborative machine learning training without sharing private data.
no code implementations • 24 Feb 2024 • Yuanzhe Peng, Jieming Bian, Jie Xu
The fusion of complementary multimodal information is crucial in computational pathology for accurate diagnostics.
no code implementations • 16 Dec 2023 • Lei Wang, Jieming Bian, Jie Xu
We introduce a novel algorithm called FedBeat (Federated Learning with Bayesian Ensemble-Assisted Transition Matrix Estimation).
no code implementations • 6 Nov 2023 • Jieming Bian, Lei Wang, Shaolei Ren, Jie Xu
Training large-scale artificial intelligence (AI) models demands significant computational power and energy, leading to increased carbon footprint with potential environmental repercussions.
no code implementations • 21 Apr 2023 • Jieming Bian, Cong Shen, Jie Xu
The use of indirect communication presents new challenges for convergence analysis and optimization, as the delay introduced by the transporters' movement creates issues for both global model dissemination and local model collection.
no code implementations • 10 Apr 2023 • Jieming Bian, Lei Wang, Kun Yang, Cong Shen, Jie Xu
In this paper, we provide theoretical analysis of hybrid FL under clients' partial participation to validate that partial participation is the key constraint on convergence speed.
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 • 14 Feb 2023 • Jieming Bian, Cong Shen, Jie Xu
In this paper, we propose a novel FL framework, named FedEx (short for FL via Model Express Delivery), that utilizes mobile transporters (e. g., Unmanned Aerial Vehicles) to establish indirect communication channels between the server and the clients.
no code implementations • 9 Jun 2022 • Jieming Bian, Jie Xu
To address this issue, the paper explores the impact of mobility on the convergence performance of asynchronous FL.
no code implementations • 15 Oct 2021 • Jieming Bian, Zhu Fu, Jie Xu
Federated learning (FL), a popular decentralized and privacy-preserving machine learning (FL) framework, has received extensive research attention in recent years.