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Federated learning (FL) enables collaborative training without pooling raw data, but standard FL relies on a central coordinator, which introduces a single point of failure and concentrates trust in the orchestration infrastructure.
BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learning
Abhijit Guha Roy, Shayan Siddiqui, Sebastian Pölsterl, Nassir Navab, and Christian Wachinger. 2019 · 1905
Earlier work this paper cites.
Decentralized Federated Learning: A Segmented Gossip Approach
Chenghao Hu, Jingyan Jiang, and Zhi Wang. 2019 · 1908
Earlier work this paper cites.
PIRATE: A Blockchain-based Secure Framework of Distributed Machine Learning in 5G Networks
Sicong Zhou, Huawei Huang, Wuhui Chen, Zibin Zheng, and Song Guo. 2019a · 1912
Earlier work this paper cites.
Secure Multi-Party Computation
Oded Goldreich. 1999 · 1999
Earlier work this paper cites.
An introduction to peer-to-peer computing
David Barkai. 2000 · 2000
Earlier work this paper cites.
FedCoin: A Peer-to-Peer Payment System for Federated Learning
Yuan Liu, Shuai Sun, Zhengpeng Ai, Shuangfeng Zhang, Zelei Liu, and Han Yu. 2020 · 2002
Earlier work this paper cites.
Identity crisis: anonymity vs reputation in P2P systems. In Proceedings Third International Conference on Peer-to-Peer Computing (P2P2003) . 134–141
S. Marti and H. Garcia-Molina. 2003 · 2003
Earlier work this paper cites.
2 P2P or Not 2 P2P?. In International Workshop on Peer-to-Peer Systems
Mema Roussopoulos, Mary Baker, David S. H. Rosenthal, Thomas J. Giuli, Petros Maniatis, and Jeffrey C. Mogul. 2003 · 2003
Earlier work this paper cites.
A Lightweight Currency Paradigm for the P2P Resource Market
Turner D. A. 2004 · 2004
Earlier work this paper cites.
Attacks on peer-to-peer networks
Baptiste Pretre. 2005 · 2005
Earlier work this paper cites.
The Health Insurance Portability and Accountability Act of 1996 (HIPAA) privacy rule: implications for clinical research
Rachel Nosowsky and Thomas J Giordano. 2006 · 2006
Earlier work this paper cites.
Distributed Subgradient Methods for Multi-Agent Optimization
Angelia Nedic and Asuman Ozdaglar. 2009 · 2008
Earlier work this paper cites.
Resilience and reliability analysis of P2P network systems
Xiaohu Li, Peng Zhao, and Linxiong Li. 2010 · 2009
Earlier work this paper cites.
Bitcoin: A Peer-to-Peer Electronic Cash System
Satoshi Nakamoto. 2009 · 2009
Earlier work this paper cites.
A Survey on Transfer Learning
Sinno Jialin Pan and Qiang Yang. 2010 · 2009
Earlier work this paper cites.
GFL: A Decentralized Federated Learning Framework Based On Blockchain
Yifan Hu, Yuhang Zhou, Jun Xiao, and Chao Wu. 2021 · 2010
Earlier work this paper cites.
Privacy-Preserving P2P Data Sharing with OneSwarm. In Proc. of SIGCOMM ’10 (New Delhi, India). Association for Computing Machinery, New York, NY, USA, 111–122
Tomas Isdal, Michael Piatek, Arvind Krishnamurthy, and Thomas Anderson. 2010 · 2010
Earlier work this paper cites.
Distributed Stochastic Subgradient Projection Algorithms for Convex Optimization
S. Ram, Angelia Nedic, and V. Veeravalli. 2010 · 2010
Earlier work this paper cites.
Data Matching: Concepts and Techniques for Record Linkage, Entity Resolution, and Duplicate Detection
Peter Christen. 2012 · 2012
Earlier work this paper cites.
Ethereum White Paper: A Next Generation Smart Contract & Decentralized Application Platform
Vitalik Buterin. 2013 · 2013
Earlier work this paper cites.
Privacy-Preserving Ridge Regression on Hundreds of Millions of Records. In IEEE Symposium on Security and Privacy . 334–348
Valeria Nikolaenko, Udi Weinsberg, Stratis Ioannidis, Marc Joye, Dan Boneh, and Nina Taft. 2013 · 2013
Earlier work this paper cites.
Privacy Preserving Back-Propagation Neural Network Learning Made Practical with Cloud Computing
Jiawei Yuan and Shucheng Yu. 2014 · 2013
Earlier work this paper cites.
IPFS - Content Addressed, Versioned, P2P File System
Juan Benet. 2014 · 2014
Earlier work this paper cites.
The Algorithmic Foundations of Differential Privacy
Cynthia Dwork and Aaron Roth. 2014 · 2014
Earlier work this paper cites.
A Fast, Minimal Memory, Consistent Hash Algorithm
John Lamping and Eric Veach. 2014 · 2014
Earlier work this paper cites.
Adversarial Active Learning. In Proceedings of the 2014 Workshop on Artificial Intelligent and Security Workshop (Scottsdale, Arizona, USA) (AISec ’14) . Association for Computing Machinery, New York, NY, USA, 3–14
Brad Miller, Alex Kantchelian, Sadia Afroz, Rekha Bachwani, Edwin Dauber, Ling Huang, Michael Carl Tschantz, Anthony D. Joseph, and J.D. Tygar. 2014 · 2014
Earlier work this paper cites.
1-bit stochastic gradient descent and its application to data-parallel distributed training of speech DNNs. In Interspeech
Frank Seide, Hao Fu, Jasha Droppo, Gang Li, and Dong Yu. 2014 · 2014
Earlier work this paper cites.
Want to scale in centralized systems? Think P2P
Anne-Marie Kermarrec and François Taiani. 2015 · 2015
Earlier work this paper cites.
Machine Learning with Adversaries: Byzantine Tolerant Gradient Descent. In Adv. in NeurIPS , I. Guyon, U. Von Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Eds.), Vol. 30. Curran Associates, Inc
Peva Blanchard, El Mahdi El Mhamdi, Rachid Guerraoui, and Julien Stainer. 2017 · 2017
Earlier work this paper cites.
Flopcoin: A cryptocurrency for computation offloading
Dimitris Chatzopoulos, Mahdieh Ahmadi, Sokol Kosta, and Pan Hui. 2017 · 2017
Earlier work this paper cites.
Algorand: Scaling Byzantine Agreements for Cryptocurrencies. In Proc. of SOSP ’17 (Shanghai, China). Association for Computing Machinery, New York, NY, USA, 51–68
Yossi Gilad, Rotem Hemo, Silvio Micali, Georgios Vlachos, and Nickolai Zeldovich. 2017 · 2017
Earlier work this paper cites.
Stephen Hardy, Wilko Henecka, Hamish Ivey-Law, Richard Nock, Giorgio Patrini, Guillaume Smith, and Brian Thorne. 2017 · 2017
Earlier work this paper cites.
Communication-Efficient Learning of Deep Networks from Decentralized Data. In Proc. of AISTATS ’17’ , Aarti Singh and Jerry Zhu (Eds.), Vol. 54. PMLR, 1273–1282
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. 2017 · 2017
Earlier work this paper cites.
Federated learning: Collaborative machine learning without centralized training data
Brendan McMahan and Daniel Ramage. 2017 · 2017
Earlier work this paper cites.
Privacy-Preserving Deep Learning via Additively Homomorphic Encryption
Le Trieu Phong, Yoshinori Aono, Takuya Hayashi, Lihua Wang, and Shiho Moriai. 2018 · 2017
Earlier work this paper cites.
ZipML: Training Linear Models with End-to-End Low Precision, and a Little Bit of Deep Learning. In Proceedings of the 34th International Conference on Machine Learning - Volume 70 (Sydney, NSW, Australia) (ICML’17) . JMLR.org, 4035–4043
Hantian Zhang, Jerry Li, Kaan Kara, Dan Alistarh, Ji Liu, and Ce Zhang. 2017 · 2017
Cited alongside, same era.
Hot-Stuff the Linear, Optimal-Resilience, One-Message BFT Devil
Ittai Abraham, Guy Gueta, and Dahlia Malkhi. 2018 · 2018
Cited alongside, same era.
Personalized and Private Peer-to-Peer Machine Learning. In Proc. of AISTATS ’18 , Amos Storkey and Fernando Perez-Cruz (Eds.), Vol. 84. PMLR, 473–481
Aurélien Bellet, Rachid Guerraoui, Mahsa Taziki, and Marc Tommasi. 2018 · 2018
Cited alongside, same era.
When Machine Learning Meets Blockchain: A Decentralized, Privacy-preserving and Secure Design. In IEEE Big Data . 1178–1187
Xuhui Chen, Jinlong Ji, Changqing Luo, Weixian Liao, and Pan Li. 2018 · 2018
Cited alongside, same era.
Blockchain Assisted Decentralized Federated Learning (BLADE-FL): Performance Analysis and Resource Allocation
Jun Li, Yumeng Shao, Kang Wei, Ming Ding, Chuan Ma, Long Shi, Zhu Han, and H. Vincent Poor. 2022c · 2021
Later among the works it cites.
A Blockchain-Based Decentralized Federated Learning Framework with Committee Consensus
Yuzheng Li, Chuan Chen, Nan Liu, Huawei Huang, Zibin Zheng, and Qiang Yan. 2021a · 2021
Later among the works it cites.
Byzantine Resistant Secure Blockchained Federated Learning at the Edge
Zonghang Li, Hongfang Yu, Tianyao Zhou, Long Luo, Mochan Fan, Zenglin Xu, and Gang Sun. 2021b · 2021
Later among the works it cites.
Decentralized federated learning of deep neural networks on non-iid data
Noa Onoszko, Gustav Karlsson, Olof Mogren, and Edvin Listo Zec. 2021 · 2021
Later among the works it cites.
The PRISMA 2020 statement: an updated guideline for reporting systematic reviews
Matthew J Page, Joanne E McKenzie, Patrick M Bossuyt, Isabelle Boutron, Tammy C Hoffmann, Cynthia D Mulrow, Larissa Shamseer, Jennifer M Tetzlaff, Elie A Akl, Sue E Brennan, et al · 2021
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Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning. In 2018 IEEE Symposium on Security and Privacy (SP) . 19–35
Matthew Jagielski, Alina Oprea, Battista Biggio, Chang Liu, Cristina Nita-Rotaru, and Bo Li. 2018 · 2018
Cited alongside, same era.
Fully Decentralized Federated Learning
Anusha Lalitha. 2018 · 2018
Cited alongside, same era.
EU General Data Protection Regulation: Changes and implications for personal data collecting companies
Christina Tikkinen-Piri, Anna Rohunen, and Jouni Markkula. 2018 · 2018
Cited alongside, same era.
ATOMO: Communication-Efficient Learning via Atomic Sparsification. In Proceedings of the 32nd International Conference on Neural Information Processing Systems (Montréal, Canada) (NIPS’18) . Curran Associates Inc., Red Hook, NY, USA, 9872–9883
Hongyi Wang, Scott Sievert, Zachary Charles, Shengchao Liu, Stephen Wright, and Dimitris Papailiopoulos. 2018 · 2018
Cited alongside, same era.
Blockchain Technology Overview
Dylan Yaga, Peter Mell, Nik Roby, and Karen Scarfone. 2018 · 2018
Cited alongside, same era.
Federated Learning with Non-IID Data
Yue Zhao, Meng Li, Liangzhen Lai, Naveen Suda, Damon Civin, and Vikas Chandra. 2018 · 2018
Cited alongside, same era.
A Little Is Enough: Circumventing Defenses for Distributed Learning. In Proc. of NeurIPS ’19 . 8632–8642
Gilad Baruch, Moran Baruch, and Yoav Goldberg. 2019 · 2019
Cited alongside, same era.
Analyzing Federated Learning through an Adversarial Lens. In Proc. of ICML , Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.), Vol. 97. PMLR, 634–643
Arjun Nitin Bhagoji, Supriyo Chakraborty, Prateek Mittal, and Seraphin Calo. 2019 · 2019
Cited alongside, same era.
Later among the works it cites.
IPLS: A Framework for Decentralized Federated Learning. In 2021 IFIP Networking Conference (IFIP Networking) . 1–6
Christodoulos Pappas, Dimitris Chatzopoulos, Spyros Lalis, and Manolis Vavalis. 2021 · 2021
Later among the works it cites.
VFChain: Enabling Verifiable and Auditable Federated Learning via Blockchain Systems
Zhe Peng, Jianliang Xu, Xiaowen Chu, Shang Gao, Yuan Yao, Rong Gu, and Yuzhe Tang. 2022 · 2021
Later among the works it cites.
Proof of Federated Learning: A Novel Energy-Recycling Consensus Algorithm
Xidi Qu, Shengling Wang, Qin Hu, and Xiuzhen Cheng. 2021b · 2021
Later among the works it cites.
Decentralized Federated Learning for UAV Networks: Architecture, Challenges, and Opportunities
Yuben Qu, Haipeng Dai, Yan Zhuang, Jiafa Chen, Chao Dong, Fan Wu, and Song Guo. 2021a · 2021
Later among the works it cites.
An incentive mechanism for cross-silo federated learning: A public goods perspective. In IEEE INFOCOM 2021-IEEE Conference on Computer Communications . IEEE, 1–10
Ming Tang and Vincent WS Wong. 2021 · 2021
Later among the works it cites.
Turning Federated Learning Systems Into Covert Channels
Gabriele Costa, Fabio Pinelli, Simone Soderi, and Gabriele Tolomei. 2022 · 2022
Later among the works it cites.
Trusted Decentralized Federated Learning. In IEEE CCNC . 1–6
Anousheh Gholami, Nariman Torkzaban, and John S. Baras. 2022 · 2022
Later among the works it cites.
Towards Effective Clustered Federated Learning: A Peer-to-peer Framework with Adaptive Neighbor Matching
Zexi Li, Jiaxun Lu, Shuang Luo, Didi Zhu, Yunfeng Shao, Yinchuan Li, Zhimeng Zhang, Yongheng Wang, and Chao Wu. 2022b · 2022
Later among the works it cites.
A State-of-the-Art Survey on Solving Non-IID Data in Federated Learning
Xiaodong Ma, Jia Zhu, Zhihao Lin, Shanxuan Chen, and Yangjie Qin. 2022 · 2022
Later among the works it cites.
The Creativity of Text-to-Image Generation. In Proceedings of the 25th International Academic Mindtrek Conference . ACM
Jonas Oppenlaender. 2022 · 2022
Later among the works it cites.
Secure Smart Communication Efficiency in Federated Learning: Achievements and Challenges
Seyedamin Pouriyeh, Osama Shahid, Reza M. Parizi, Quan Z. Sheng, Gautam Srivastava, Liang Zhao, and Mohammad Nasajpour. 2022 · 2022
Later among the works it cites.
Blockchain-Enabled Federated Learning: A Survey
Youyang Qu, Md Palash Uddin, Chenquan Gan, Yong Xiang, Longxiang Gao, and John Yearwood. 2022 · 2022
Later among the works it cites.
Hierarchical Text-Conditional Image Generation with CLIP Latents
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. 2022 · 2022
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GossipFL: A Decentralized Federated Learning Framework With Sparsified and Adaptive Communication
Zhenheng Tang, Shaohuai Shi, Bo Li, and Xiaowen Chu. 2023 · 2022
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Peer-to-Peer Variational Federated Learning Over Arbitrary Graphs
Xinghan Wang, Anusha Lalitha, Tara Javidi, and Farinaz Koushanfar. 2022 · 2022
Later among the works it cites.
A practical cross-device federated learning framework over 5G networks
Wenti Yang, Naiyu Wang, Zhitao Guan, Longfei Wu, Xiaojiang Du, and Mohsen Guizani. 2022 · 2022
Later among the works it cites.
Trustworthy Federated Learning via Blockchain
Zhanpeng Yang, Yuanming Shi, Yong Zhou, Zixin Wang, and Kai Yang. 2023 · 2022
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PVD-FL: A Privacy-Preserving and Verifiable Decentralized Federated Learning Framework
Jiaqi Zhao, Hui Zhu, Fengwei Wang, Rongxing Lu, Zhe Liu, and Hui Li. 2022 · 2022
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