SplitFed: When Federated Learning Meets Split Learning

25 April 2020

Chandra Thapa

Pathum Chamikara Mahawaga Arachchige

Papers citing "SplitFed: When Federated Learning Meets Split Learning"

24 / 74 papers shown

Title
Combined Federated and Split Learning in Edge Computing for Ubiquitous Intelligence in Internet of Things: State of the Art and Future Directions Qiang Duan Shijing Hu Ruijun Deng Zhihui Lu FedML 31 61 0 20 Jul 2022
BlindFL: Vertical Federated Machine Learning without Peeking into Your Data Fangcheng Fu Huanran Xue Yong Cheng Yangyu Tao Bin Cui FedML 23 59 0 16 Jun 2022
Multi-Task Distributed Learning using Vision Transformer with Random Patch Permutation Sangjoon Park Jong Chul Ye FedML MedIm 42 19 0 07 Apr 2022
Enabling All In-Edge Deep Learning: A Literature Review Praveen Joshi Mohammed Hasanuzzaman Chandra Thapa Haithem Afli T. Scully 31 22 0 07 Apr 2022
No Free Lunch Theorem for Security and Utility in Federated Learning Xiaojin Zhang Hanlin Gu Lixin Fan Kai Chen Qiang Yang FedML 16 64 0 11 Mar 2022
Similarity-based Label Inference Attack against Training and Inference of Split Learning Junlin Liu Xinchen Lyu Qimei Cui Xiaofeng Tao FedML 29 26 0 10 Mar 2022
LSTMSPLIT: Effective SPLIT Learning based LSTM on Sequential Time-Series Data Lianlian Jiang Yuexuan Wang Wenyi Zheng Chao Jin Zengxiang Li Sin Gee Teo AI4TS 25 10 0 08 Mar 2022
On the Convergence of Heterogeneous Federated Learning with Arbitrary Adaptive Online Model Pruning Hanhan Zhou Tian-Shing Lan Guru Venkataramani Wenbo Ding FedML 26 6 0 27 Jan 2022
FLoBC: A Decentralized Blockchain-Based Federated Learning Framework M. C. Ghanem Fadi Dawoud Habiba Gamal E. Soliman Hossam Sharara Tamer El-Batt 19 10 0 22 Dec 2021
Splitfed learning without client-side synchronization: Analyzing client-side split network portion size to overall performance Praveen Joshi Chandra Thapa S. Çamtepe M. Hasanuzzamana T. Scully Haithem Afli FedML 40 24 0 19 Sep 2021
Decentralized Deep Learning for Multi-Access Edge Computing: A Survey on Communication Efficiency and Trustworthiness Yuwei Sun H. Ochiai Hiroshi Esaki FedML 74 45 0 30 Jul 2021
FedAdapt: Adaptive Offloading for IoT Devices in Federated Learning Di Wu R. Ullah P. Harvey Peter Kilpatrick I. Spence Blesson Varghese 40 78 0 09 Jul 2021
Multi-VFL: A Vertical Federated Learning System for Multiple Data and Label Owners Vaikkunth Mugunthan P. Goyal Lalana Kagal FedML 24 9 0 10 Jun 2021
PyVertical: A Vertical Federated Learning Framework for Multi-headed SplitNN Daniele Romanini A. Hall Pavlos Papadopoulos Tom Titcombe Abbas Ismail Tudor Cebere R. Sandmann Robin Roehm Michael A. Hoeh FedML MU 13 90 0 01 Apr 2021
Evaluation and Optimization of Distributed Machine Learning Techniques for Internet of Things Yansong Gao Minki Kim Chandra Thapa Sharif Abuadbba Zhi-Li Zhang S. Çamtepe Hyoungshick Kim Surya Nepal 28 59 0 03 Mar 2021
Towards Personalized Federated Learning A. Tan Han Yu Li-zhen Cui Qiang Yang FedML AI4CE 209 840 0 01 Mar 2021
Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning Shaoxiong Ji Yue Tan Teemu Saravirta Zhiqin Yang Yixin Liu Lauri Vasankari Shirui Pan Guodong Long A. Walid FedML 37 76 0 25 Feb 2021
Unleashing the Tiger: Inference Attacks on Split Learning Dario Pasquini G. Ateniese M. Bernaschi FedML 26 147 0 04 Dec 2020
Advancements of federated learning towards privacy preservation: from federated learning to split learning Chandra Thapa Pathum Chamikara Mahawaga Arachchige S. Çamtepe FedML 16 82 0 25 Nov 2020
SplitEasy: A Practical Approach for Training ML models on Mobile Devices Kamalesh Palanisamy Vivek Khimani Moin Hussain Moti Dimitris Chatzopoulos 14 20 0 09 Nov 2020
FedSL: Federated Split Learning on Distributed Sequential Data in Recurrent Neural Networks Ali Abedi Shehroz S. Khan FedML 31 53 0 06 Nov 2020
HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients Enmao Diao Jie Ding Vahid Tarokh FedML 26 543 0 03 Oct 2020
NoPeek: Information leakage reduction to share activations in distributed deep learning Praneeth Vepakomma Abhishek Singh O. Gupta Ramesh Raskar MIACV FedML 21 84 0 20 Aug 2020
Frankenstein: Learning Deep Face Representations using Small Data Guosheng Hu Xiaojiang Peng Yongxin Yang Timothy M. Hospedales Jakob Verbeek CVBM 74 122 0 21 Mar 2016