FedCompass: Efficient Cross-Silo Federated Learning on Heterogeneous
Client Devices using a Computing Power Aware Scheduler

FedCompass: Efficient Cross-Silo Federated Learning on Heterogeneous Client Devices using a Computing Power Aware Scheduler

26 September 2023

Pranshu Chaturvedi

Gagandeep Singh

Volodymyr V. Kindratenko

Ravi K. Madduri

Papers citing "FedCompass: Efficient Cross-Silo Federated Learning on Heterogeneous Client Devices using a Computing Power Aware Scheduler"

9 / 9 papers shown

Title
Sparsification Under Siege: Defending Against Poisoning Attacks in Communication-Efficient Federated Learning Zhiyong Jin Runhua Xu Chong Li Y. Liu Jianxin Li AAML FedML 37 0 0 30 Apr 2025
Advances in Privacy Preserving Federated Learning to Realize a Truly Learning Healthcare System Ravi K. Madduri Zilinghan Li Tarak Nandi Kibaek Kim Minseok Ryu Alex Rodriguez 35 1 0 29 Sep 2024
Advances in APPFL: A Comprehensive and Extensible Federated Learning Framework Zilinghan Li Shilan He Ze Yang Minseok Ryu Kibaek Kim Ravi K. Madduri FedML 57 5 0 17 Sep 2024
Asynchronous Federated Stochastic Optimization for Heterogeneous Objectives Under Arbitrary Delays Charikleia Iakovidou Kibaek Kim FedML 35 2 0 16 May 2024
Secure Federated Learning Across Heterogeneous Cloud and High-Performance Computing Resources -- A Case Study on Federated Fine-tuning of LLaMA 2 Zilinghan Li Shilan He Pranshu Chaturvedi Volodymyr V. Kindratenko Eliu A. Huerta Kibaek Kim Ravi K. Madduri FedML 12 3 0 19 Feb 2024
Asynchronous Federated Learning on Heterogeneous Devices: A Survey Chenhao Xu Youyang Qu Yong Xiang Longxiang Gao FedML 101 241 0 09 Sep 2021
FedAR: Activity and Resource-Aware Federated Learning Model for Distributed Mobile Robots Ahmed Imteaj M. Amini 80 51 0 11 Jan 2021
Straggler-Resilient Federated Learning: Leveraging the Interplay Between Statistical Accuracy and System Heterogeneity Amirhossein Reisizadeh Isidoros Tziotis Hamed Hassani Aryan Mokhtari Ramtin Pedarsani FedML 172 99 0 28 Dec 2020
TorchIO: A Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning Fernando Pérez-García Rachel Sparks Sébastien Ourselin MedIm LM&MA 138 427 0 09 Mar 2020