Fetching the paper…
Reading the bibliography…
With the technological advances in machine learning, effective ways are available to process the huge amount of data generated in real life.
Ethereum: A secure decentralised generalised transaction ledger
Gavin Wood et al · 2014
Earlier work this paper cites.
Federated optimization: Distributed machine learning for on-device intelligence
Jakub Konečnỳ, H Brendan McMahan, Daniel Ramage, and Peter Richtárik · 2016
Earlier work this paper cites.
Federated Learning: Strategies for Improving Communication Efficiency
Jakub Konečný, H. Brendan McMahan, Felix X. Yu, Peter Richtárik, Ananda Theertha Suresh, and Dave Bacon · 2016
Earlier work this paper cites.
Federated Optimization: Distributed Machine Learning for On-Device Intelligence
Jakub Konečný, H. Brendan McMahan, Daniel Ramage, and Peter Richtárik · 2016
Earlier work this paper cites.
Communication-efficient learning of deep networks from decentralized data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas · 2017
Earlier work this paper cites.
Differentially Private Federated Learning: A Client Level Perspective
Robin C. Geyer, Tassilo Klein, and Moin Nabi · 2017
Earlier work this paper cites.
Federated learning for mobile keyboard prediction
Andrew Hard, Kanishka Rao, Rajiv Mathews, Swaroop Ramaswamy, Françoise Beaufays, Sean Augenstein, Hubert Eichner, Chloé Kiddon, and Daniel Ramage · 2018
Earlier work this paper cites.
How to backdoor federated learning
Eugene Bagdasaryan, Andreas Veit, Yiqing Hua, Deborah Estrin, and Vitaly Shmatikov · 2018
Earlier work this paper cites.
Federated learning with non-iid data
Yue Zhao, Meng Li, Liangzhen Lai, Naveen Suda, Damon Civin, and Vikas Chandra · 2018
Earlier work this paper cites.
Federated learning of predictive models from federated electronic health records
Theodora S Brisimi, Ruidi Chen, Theofanie Mela, Alex Olshevsky, Ioannis Ch Paschalidis, and Wei Shi · 2018
Earlier work this paper cites.
Chained anomaly detection models for federated learning: An intrusion detection case study
Davy Preuveneers, Vera Rimmer, Ilias Tsingenopoulos, Jan Spooren, Wouter Joosen, and Elisabeth Ilie-Zudor · 2018
Earlier work this paper cites.
Towards Federated Learning at Scale: System Design
Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloe Kiddon, Jakub Konečný, Stefano Mazzocchi, H. Brendan McMahan, Timon Van Overveldt, David Petrou, Daniel Ramage, and Jason Roselander · 2019
Earlier work this paper cites.
BAFFLE : Blockchain Based Aggregator Free Federated Learning
Paritosh Ramanan and Kiyoshi Nakayama · 2019
Earlier work this paper cites.
Blockchain-based node-aware dynamic weighting methods for improving federated learning performance
You Jun Kim and Choong Seon Hong · 2019
Earlier work this paper cites.
Advances and Open Problems in Federated Learning
Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Keith Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D’Oliveira, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaid Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konečný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Mariana Raykova, Hang Qi, Daniel Ramage, Ramesh Raskar, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, and Sen Zhao · 2019
Earlier work this paper cites.
Fair Resource Allocation in Federated Learning
Tian Li, Maziar Sanjabi, Ahmad Beirami, and Virginia Smith · 2019
Earlier work this paper cites.
Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge
Takayuki Nishio and Ryo Yonetani · 2019
Earlier work this paper cites.
Federated machine learning: Concept and applications
Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong · 2019
Earlier work this paper cites.
Differential privacy-enabled federated learning for sensitive health data
Olivia Choudhury, Aris Gkoulalas-Divanis, Theodoros Salonidis, Issa Sylla, Yoonyoung Park, Grace Hsu, and Amar Das · 2019
Earlier work this paper cites.
A hybrid approach to privacy-preserving federated learning
Stacey Truex, Thomas Steinke, Nathalie Baracaldo, Heiko Ludwig, Yi Zhou, Ali Anwar, and Rui Zhang · 2019
Earlier work this paper cites.
FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization
Amirhossein Reisizadeh, Aryan Mokhtari, Hamed Hassani, Ali Jadbabaie, and Ramtin Pedarsani · 2019
Earlier work this paper cites.
Federated learning for healthcare informatics
Jie Xu and Fei Wang · 2019
Earlier work this paper cites.
Bitcoin: A peer-to-peer electronic cash system
Satoshi Nakamoto · 2019
Earlier work this paper cites.
Blockchain technology in healthcare: a systematic review
Cornelius C Agbo, Qusay H Mahmoud, and J Mikael Eklund · 2019
Earlier work this paper cites.
A Survey of Blockchain Technology Applied to Smart Cities: Research Issues and Challenges
Junfeng Xie, Helen Tang, Tao Huang, F. Richard Yu, Renchao Xie, Jiang Liu, and Yunjie Liu · 2019
Earlier work this paper cites.
Mechanism Design for An Incentive-aware Blockchain-enabled Federated Learning Platform
Kentaroh Toyoda and Allan N. Zhang · 2019
Earlier work this paper cites.
Deepchain: Auditable and privacy-preserving deep learning with blockchain-based incentive
Jiasi Weng, Jian Weng, Jilian Zhang, Ming Li, Yue Zhang, and Weiqi Luo · 2019
Earlier work this paper cites.
FLChain: A Blockchain for Auditable Federated Learning with Trust and Incentive
Xianglin Bao, Cheng Su, Yan Xiong, Wenchao Huang, and Yifei Hu · 2019
Earlier work this paper cites.
FLchain: Federated Learning via MEC-enabled Blockchain Network
Umer Majeed and Choong Seon Hong · 2019
Earlier work this paper cites.
DeepChain: Auditable and Privacy-Preserving Deep Learning with Blockchain-based Incentive
Jiasi Weng, Jian Weng, Jilian Zhang, Ming Li, Yue Zhang, and Weiqi Luo · 2019
Earlier work this paper cites.
A blockchain-orchestrated federated learning architecture for healthcare consortia
Jonathan Passerat-Palmbach, Tyler Farnan, Robert Miller, Marielle S Gross, Heather Leigh Flannery, and Bill Gleim · 2019
Earlier work this paper cites.
Record and reward federated learning contributions with blockchain
Ismael Martinez, Sreya Francis, and Abdelhakim Senhaji Hafid · 2019
Earlier work this paper cites.
Incentive mechanism for reliable federated learning: A joint optimization approach to combining reputation and contract theory
Jiawen Kang, Zehui Xiong, Dusit Niyato, Shengli Xie, and Junshan Zhang · 2019
Cited alongside, same era.
Privacy-Preserving Blockchain Based Federated Learning with Differential Data Sharing
Anudit Nagar · 2019
Cited alongside, same era.
Adaptive federated learning in resource constrained edge computing systems
Shiqiang Wang, Tiffany Tuor, Theodoros Salonidis, Kin K Leung, Christian Makaya, Ting He, and Kevin Chan · 2019
Cited alongside, same era.
In-edge ai: Intelligentizing mobile edge computing, caching and communication by federated learning
Xiaofei Wang, Yiwen Han, Chenyang Wang, Qiyang Zhao, Xu Chen, and Min Chen · 2019
Cited alongside, same era.
Privacy-aware service placement for mobile edge computing via federated learning
Yongfeng Qian, Long Hu, Jing Chen, Xin Guan, Mohammad Mehedi Hassan, and Abdulhameed Alelaiwi · 2019
Cited alongside, same era.
Federated Learning with Blockchain for Autonomous Vehicles: Analysis and Design Challenges
Shiva Raj Pokhrel and Jinho Choi · 2020
Later among the works it cites.
Privacy-Preserving Blockchain-Based Federated Learning for IoT Devices
Yang Zhao, Jun Zhao, Linshan Jiang, Rui Tan, Dusit Niyato, Zengxiang Li, Lingjuan Lyu, and Yingbo Liu · 2020
Later among the works it cites.
Blockchain Empowered Asynchronous Federated Learning for Secure Data Sharing in Internet of Vehicles
Yunlong Lu, Xiaohong Huang, Ke Zhang, Sabita Maharjan, and Yan Zhang · 2020
Later among the works it cites.
Blockchain and federated learning-based distributed computing defence framework for sustainable society
Pradip Kumar Sharma, Jong Hyuk Park, and Kyungeun Cho · 2020
Later among the works it cites.
Exploiting Unintended Property Leakage in Blockchain-Assisted Federated Learning for Intelligent Edge Computing
Meng Shen, Huan Wang, Bin Zhang, Liehuang Zhu, Ke Xu, Qi Li, and Xiaojiang Du · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Poster: A reliable and accountable privacy-preserving federated learning framework using the blockchain
Sana Awan, Fengjun Li, Bo Luo, and Mei Liu · 2019
Cited alongside, same era.
Differential privacy-enabled federated learning for sensitive health data
Olivia Choudhury, Aris Gkoulalas-Divanis, Theodoros Salonidis, Issa Sylla, Yoonyoung Park, Grace Hsu, and Amar Das · 2019
Cited alongside, same era.
Robust and Communication-Efficient Federated Learning from Non-i.i.d. Data
Felix Sattler, Simon Wiedemann, Klaus Robert Muller, and Wojciech Samek · 2020
Cited alongside, same era.
FedCoin: A Peer-to-Peer Payment System for Federated Learning
Yuan Liu, Shuai Sun, Zhengpeng Ai, Shuangfeng Zhang, Zelei Liu, and Han Yu · 2020
Cited alongside, same era.
Federated learning via over-the-air computation
Kai Yang, Tao Jiang, Yuanming Shi, and Zhi Ding · 2020
Cited alongside, same era.
Convergence Time Minimization of Federated Learning over Wireless Networks
Mingzhe Chen, H. Vincent Poor, Walid Saad, and Shuguang Cui · 2020
Cited alongside, same era.
Federated Learning for Wireless Communications: Motivation, Opportunities, and Challenges
Solmaz Niknam, Harpreet S. Dhillon, and Jeffrey H. Reed · 2020
Cited alongside, same era.
Blockchain-based Federated Learning for Failure Detection in Industrial IoT
Weishan Zhang, Qinghua Lu, Qiuyu Yu, Zhaotong Li, Yue Liu, Sin Kit Lo, Shiping Chen, Xiwei Xu, and Liming Zhu · 2020
Later among the works it cites.
CREAT: Blockchain-assisted Compression Algorithm of Federated Learning for Content Caching in Edge Computing
Laizhong Cui, Xiaoxin Su, Zhongxing Ming, Ziteng Chen, Shu Yang, Yipeng Zhou, and Wei Xiao · 2020
Later among the works it cites.
Towards blockchain-based reputation-aware federated learning
Muhammad Habib Ur Rehman, Khaled Salah, Ernesto Damiani, and Davor Svetinovic · 2020
Later among the works it cites.
Reliable Federated Learning for Mobile Networks
Jiawen Kang, Zehui Xiong, Dusit Niyato, Yuze Zou, Yang Zhang, and Mohsen Guizani · 2020
Later among the works it cites.
Hybrid blockchain-based resource trading system for federated learning in edge computing
Sizheng Fan, Hongbo Zhang, Yuchen Zeng, and Wei Cai · 2020
Later among the works it cites.
IFLBC: On the Edge Intelligence Using Federated Learning Blockchain Network
Ronald Doku and Danda B. Rawat · 2020
Later among the works it cites.
Federated Learning with Blockchain for Autonomous Vehicles: Analysis and Design Challenges
Shiva Raj Pokhrel and Jinho Choi · 2020
Later among the works it cites.
Scalable and Communication-efficient Decentralized Federated Edge Learning with Multi-blockchain Framework
Jiawen Kang, Zehui Xiong, Chunxiao Jiang, Yi Liu, Song Guo, Yang Zhang, Dusit Niyato, Cyril Leung, and Chunyan Miao · 2020
Later among the works it cites.
Blockchained on-device federated learning
Hyesung Kim, Jihong Park, Mehdi Bennis, and Seong Lyun Kim · 2020
Later among the works it cites.
Convergence of Edge Computing and Deep Learning: A Comprehensive Survey
Xiaofei Wang, Yiwen Han, Victor C.M. Leung, Dusit Niyato, Xueqiang Yan, and Xu Chen · 2020
Later among the works it cites.
Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challenges
Latif U. Khan, Walid Saad, Zhu Han, Ekram Hossain, and Choong Seon Hong · 2020
Later among the works it cites.
Fltrust: Byzantine-robust federated learning via trust bootstrapping
Xiaoyu Cao, Minghong Fang, Jia Liu, and Neil Zhenqiang Gong · 2020
Later among the works it cites.
Jun Li, Yumeng Shao, Kang Wei, Ming Ding, Chuan Ma, Long Shi, Zhu Han, and H Vincent Poor · 2021
Closest in time.
Blockchain-enabled federated learning: A survey
Cheng Li, Yong Yuan, and Fei-Yue Wang · 2021
Closest in time.
A survey on security and privacy of federated learning
Viraaji Mothukuri, Reza M. Parizi, Seyedamin Pouriyeh, Yan Huang, Ali Dehghantanha, and Gautam Srivastava · 2021
Closest in time.
Towards on-device federated learning: A direct acyclic graph-based blockchain approach
Mingrui Cao, Long Zhang, and Bin Cao · 2021
Closest in time.
Blockfla: Accountable federated learning via hybrid blockchain architecture
Harsh Bimal Desai, Mustafa Safa Ozdayi, and Murat Kantarcioglu · 2021
Closest in time.
Blockchain-federated-learning and deep learning models for covid-19 detection using ct imaging
Rajesh Kumar, Abdullah Aman Khan, Jay Kumar, A Zakria, Noorbakhsh Amiri Golilarz, Simin Zhang, Yang Ting, Chengyu Zheng, and WenYong Wang · 2021
Closest in time.
Blockchain and federated edge learning for privacy-preserving mobile crowdsensing
Qin Hu, Zhilin Wang, Minghui Xu, and Xiuzhen Cheng · 2021
Closest in time.
Bc-edgefl: Defensive transmission model based on blockchain assisted reinforced federated learning in iiot environment
Peiying Zhang, Yanrong Hong, Neeraj Kumar, Mamoun Alazab, Mohammad Dahman Alshehri, and Chunxiao Jiang · 2021
Closest in time.
Blockchain-empowered decentralized cross-domain federated learning for 5g-enabled uavs
Chaosheng Feng, Bin Liu, Keping Yu, Sotirios K Goudos, and Shaohua Wan · 2021
Closest in time.
Energy-aware blockchain and federated learning-supported vehicular networks
Moayad Aloqaily, Ismaeel Al Ridhawi, and Mohsen Guizani · 2021
Closest in time.
Agent architecture of an intelligent medical system based on federated learning and blockchain technology
Dawid Połap, Gautam Srivastava, and Keping Yu · 2021
Closest in time.
Privacy-preserving blockchain-based federated learning for traffic flow prediction
Yuanhang Qi, M Shamim Hossain, Jiangtian Nie, and Xuandi Li · 2021
Closest in time.
Siren: Byzantine-robust federated learning via proactive alarming
Hanxi Guo, Hao Wang, Tao Song, Yang Hua, Zhangcheng Lv, Xiulang Jin, Zhengui Xue, Ruhui Ma, and Haibing Guan · 2021
Closest in time.
Byzantine-robust aggregation in federated learning empowered industrial iot
Shenghui Li, Edith Ngai, and Thiemo Voigt · 2021
Closest in time.
Signguard: Byzantine-robust federated learning through collaborative malicious gradient filtering
Jian Xu, Shao-Lun Huang, Linqi Song, and Tian Lan · 2021
Closest in time.