FAST: Enhancing Federated Learning Through Adaptive Data Sampling and Local Training

[PDF] arxiv.org

Communication-Efficient Large-Scale Distributed Deep Learning: A Comprehensive Survey

F Liang, Z Zhang, H Lu, V Leung, Y Guo… - arXiv preprint arXiv …, 2024 - arxiv.org
With the rapid growth in the volume of data sets, models, and devices in the domain of deep
learning, there is increasing attention on large-scale distributed deep learning. In contrast to …
被引用次数：4 相关文章 所有 2 个版本
[PDF] springer.com

Comparative analysis of open-source federated learning frameworks-a literature-based survey and review

P Riedel, L Schick, R von Schwerin, M Reichert… - International Journal of …, 2024 - Springer
Abstract While Federated Learning (FL) provides a privacy-preserving approach to analyze
sensitive data without centralizing training data, the field lacks an detailed comparison of …
被引用次数：5 相关文章 所有 2 个版本

Take Your Pick: Enabling Effective Distributed Learning Within Low-Dimensional Feature Space

G Zhu, X Liu, S Tang, J Niu, X Wu… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Personalized federated learning (PFL) is a popular distributed learning framework that
allows clients to have different models and has many applications where clients' data are in …
相关文章 所有 2 个版本
[PDF] arxiv.org

Many Hands Make Light Work: Accelerating Edge Inference via Multi-Client Collaborative Caching

W Liang, J Liu, H Xu, C Qiao, L Huang - arXiv preprint arXiv:2412.10382, 2024 - arxiv.org
Edge inference is a technology that enables real-time data processing and analysis on
clients near the data source. To ensure compliance with the Service-Level Objectives …
相关文章 所有 2 个版本