SplitEasy: A Practical Approach for Training ML models on Mobile Devices

9 November 2020

Papers citing "SplitEasy: A Practical Approach for Training ML models on Mobile Devices"

10 / 10 papers shown

Title
Training Machine Learning models at the Edge: A Survey Aymen Rayane Khouas Mohamed Reda Bouadjenek Hakim Hacid Sunil Aryal 29 10 0 05 Mar 2024
Workflow Optimization for Parallel Split Learning Joana Tirana Dimitra Tsigkari Georgios Iosifidis Dimitris Chatzopoulos 14 4 0 01 Feb 2024
MP-SL: Multihop Parallel Split Learning Joana Tirana S. Lalis Dimitris Chatzopoulos 32 3 0 31 Jan 2024
An Efficient Split Fine-tuning Framework for Edge and Cloud Collaborative Learning S. Shi Qing Yang Yang Xiang Shuhan Qi X. Wang 15 1 0 30 Nov 2022
TorchFL: A Performant Library for Bootstrapping Federated Learning Experiments Vivek Khimani S. Jabbari FedML AI4CE 6 0 0 01 Nov 2022
Similarity-based Label Inference Attack against Training and Inference of Split Learning Junlin Liu Xinchen Lyu Qimei Cui Xiaofeng Tao FedML 24 26 0 10 Mar 2022
AdaSplit: Adaptive Trade-offs for Resource-constrained Distributed Deep Learning Ayush Chopra Surya Kant Sahu Abhishek Singh Abhinav Java Praneeth Vepakomma Vivek Sharma Ramesh Raskar 32 26 0 02 Dec 2021
Communication and Computation Reduction for Split Learning using Asynchronous Training Xing Chen Jingtao Li C. Chakrabarti 11 28 0 20 Jul 2021
IPLS : A Framework for Decentralized Federated Learning C. Pappas Dimitris Chatzopoulos S. Lalis M. Vavalis VLM 11 66 0 06 Jan 2021
Unleashing the Tiger: Inference Attacks on Split Learning Dario Pasquini G. Ateniese M. Bernaschi FedML 21 147 0 04 Dec 2020