Fetching the paper…
Reading the bibliography…
Motivated by the explosive computing capabilities at end user equipments, as well as the growing privacy concerns over sharing sensitive raw data, a new machine learning paradigm, named federated learning (FL) has emerged.
M. Jelasity, “Gossip,” in Self-organising Software . Springer, 2011, pp. 139–162
2011
Earlier work this paper cites.
P. Fairley, “Blockchain world - feeding the blockchain beast if bitcoin ever does go mainstream, the electricity needed to sustain it will be enormous,” IEEE Spectrum , vol. 54, no. 10, pp. 36–59, 2017
2017
Earlier work this paper cites.
X. Bao, C. Su, Y. Xiong, W. Huang, and Y. Hu, “Flchain: A blockchain for auditable federated learning with trust and incentive,” in 2019 5th International Conference on Big Data Computing and Communications (BIGCOM) , 2019, pp. 151–159
2019
Earlier work this paper cites.
S. Wang, “Blockfedml: Blockchained federated machine learning systems,” in 2019 International Conference on Intelligent Computing, Automation and Systems (ICICAS) , 2019, pp. 751–756
2019
Earlier work this paper cites.
L. Ismail, H. Materwala, and S. Zeadally, “Lightweight blockchain for healthcare,” IEEE Access , vol. 7, pp. 149 935–149 951, 2019
2019
Earlier work this paper cites.
C. Ma, J. Li, M. Ding, H. H. Yang, F. Shu, T. Q. S. Quek, and H. V. Poor, “On safeguarding privacy and security in the framework of federated learning,” IEEE Network , vol. 34, no. 4, pp. 242–248, 2020
2020
Cited alongside, same era.
Z. Liu, P. Longa, G. C. C. F. Pereira, O. Reparaz, and H. Seo, “Four ℚ \mathbb{Q} q on embedded devices with strong countermeasures against side-channel attacks,” IEEE Transactions on Dependable and Secure Computing , vol. 17, no. 3, pp. 536–549, 2020
2020
Cited alongside, same era.
Y. Chen, X. Qin, J. Wang, C. Yu, and W. Gao, “Fedhealth: A federated transfer learning framework for wearable healthcare,” IEEE Intelligent Systems , vol. 35, no. 4, pp. 83–93, 2020
2020
Cited alongside, same era.
T. Li, A. K. Sahu, A. Talwalkar, and V. Smith, “Federated learning: Challenges, methods, and future directions,” IEEE Signal Processing Magazine , vol. 37, no. 3, pp. 50–60, 2020
2020
Cited alongside, same era.
Y. Lu, X. Huang, Y. Dai, S. Maharjan, and Y. Zhang, “Blockchain and federated learning for privacy-preserved data sharing in industrial iot,” IEEE Transactions on Industrial Informatics , vol. 16, no. 6, pp. 4177–4186, 2020
2020
Closest in time.
P. K. Sharma, J. H. Park, and K. Cho, “Blockchain and federated learning-based distributed computing defence framework for sustainable society,” Sustainable Cities and Society , vol. 59, p. 102220, 2020
2020
Closest in time.
S. Otoum, I. Al Ridhawi, and H. Mouftah, “Blockchain-supported federated learning for trustworthy vehicular networks,” 12 2020, pp. 1–6
2020
Closest in time.
Y. Qu, S. R. Pokhrel, S. Garg, L. Gao, and Y. Xiang, “A blockchained federated learning framework for cognitive computing in industry 4.0 networks,” IEEE Transactions on Industrial Informatics , vol. 17, no. 4, pp. 2964–2973, 2021
2021
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
H. Kim, J. Park, M. Bennis, and S. Kim, “Blockchained on-device federated learning,” IEEE Communications Letters , vol. 24, no. 6, pp. 1279–1283, 2020
2020
Cited alongside, same era.
2021
Closest in time.