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In recent years, Federated Learning (FL) has gained relevance in training collaborative models without sharing sensitive data.
A. Nedic and A. Ozdaglar, “Distributed subgradient methods for multi-agent optimization,” IEEE Transactions on Automatic Control , vol. 54, no. 1, pp. 48–61, 2009
2009
Earlier work this paper cites.
A. Krizhevsky, “Learning multiple layers of features from tiny images,” 2009. [Online]. Available: https://www.cs.toronto.edu/~kriz/learning-features-2009-TR.pdf
2009
Earlier work this paper cites.
A. Go, R. Bhayani, and L. Huang, “Twitter sentiment classification using distant supervision,” 2009. [Online]. Available: https://www-cs.stanford.edu/people/alecmgo/papers/TwitterDistantSupervision09.pdf
2009
Earlier work this paper cites.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “ImageNet: A large-scale hierarchical image database,” in IEEE Conference on Computer Vision and Pattern Recognition , 2009, pp. 248–255
2009
Earlier work this paper cites.
G. F. Riley and T. R. Henderson, The ns-3 Network Simulator . Springer Berlin Heidelberg, 2010, pp. 15–34
2010
Earlier work this paper cites.
K. H. Zou, A. Liu, A. I. Bandos, L. Ohno-Machado, and H. E. Rockette, Statistical Evaluation of Diagnostic Performance , 1st ed. CRC, 2011
2011
Earlier work this paper cites.
E. Cho, S. A. Myers, and J. Leskovec, “Friendship and mobility: User movement in location-based social networks,” in 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , 2011, p. 1082–1090
2011
Earlier work this paper cites.
A. L. Maas, R. E. Daly, P. T. Pham, D. Huang, A. Y. Ng, and C. Potts, “Learning word vectors for sentiment analysis,” in 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies , 2011, p. 142–150
2011
Earlier work this paper cites.
L. Deng, “The MNIST database of handwritten digit images for machine learning research,” IEEE Signal Processing Magazine , vol. 29, no. 6, pp. 141–142, 2012
2012
Earlier work this paper cites.
A. Geiger, P. Lenz, and R. Urtasun, “Are we ready for autonomous driving? the kitti vision benchmark suite,” in Conference on Computer Vision and Pattern Recognition (CVPR) , 2012
2012
Earlier work this paper cites.
W. Shi, Q. Ling, K. Yuan, G. Wu, and W. Yin, “On the linear convergence of the admm in decentralized consensus optimization,” IEEE Transactions on Signal Processing , vol. 62, no. 7, pp. 1750–1761, 2014
2014
Earlier work this paper cites.
S. Candemir et al. , “Lung segmentation in chest radiographs using anatomical atlases with nonrigid registration,” IEEE Transactions on Medical Imaging , vol. 33, no. 2, pp. 577–590, 2014
2014
Earlier work this paper cites.
S. Jaeger et al. , “Automatic tuberculosis screening using chest radiographs,” IEEE Transactions on Medical Imaging , vol. 33, no. 2, pp. 233–245, 2014
2014
Earlier work this paper cites.
O. Puñal, C. Pereira, A. Aguiar, and J. Gross, “CRAWDAD dataset uportorwthaachen/vanetjamming2014 (v. 2014-05-12),” 2014
2014
Earlier work this paper cites.
W. Shi, Q. Ling, G. Wu, and W. Yin, “EXTRA: An exact first-order algorithm for decentralized consensus optimization,” SIAM Journal on Optimization , vol. 25, no. 2, pp. 944–966, 2015
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
B. H. Menze et al. , “The multimodal brain tumor image segmentation benchmark (brats),” IEEE Transactions on Medical Imaging , vol. 34, no. 10, pp. 1993–2024, 2015
2015
Earlier work this paper cites.
D. Yang, D. Zhang, V. W. Zheng, and Z. Yu, “Modeling user activity preference by leveraging user spatial temporal characteristics in lbsns,” IEEE Transactions on Systems, Man, and Cybernetics: Systems , vol. 45, no. 1, pp. 129–142, 2015
2015
Earlier work this paper cites.
F. M. Harper and J. A. Konstan, “The MovieLens Datasets: History and context,” ACM Trans. Interact. Intell. Syst. , vol. 5, 2015
2015
Earlier work this paper cites.
2016
Earlier work this paper cites.
Y.-T. Chow, W. Shi, T. Wu, and W. Yin, “Expander graph and communication-efficient decentralized optimization,” in 50th Asilomar Conference on Signals, Systems and Computers , 2016, pp. 1715–1720
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
K. Yuan, Q. Ling, and W. Yin, “On the convergence of decentralized gradient descent,” SIAM Journal on Optimization , vol. 26, no. 3, pp. 1835–1854, 2016
2016
Earlier work this paper cites.
S. Zhu and B. Chen, “Quantized consensus by the ADMM: Probabilistic versus deterministic quantizers,” IEEE Transactions on Signal Processing , vol. 64, no. 7, pp. 1700–1713, 2016
2016
Earlier work this paper cites.
N. C. for Biotechnology Information, “Geo dataset browser,” www.ncbi.nlm.nih.gov, 2016. [Online]. Available: https://www.ncbi.nlm.nih.gov/sites/GDSbrowser/
2016
Earlier work this paper cites.
“Avito context ad clicks,” Kaggle.com, 2016. [Online]. Available: https://www.kaggle.com/c/avito-context-ad-clicks
2016
Earlier work this paper cites.
J. Yin, X. Cui, and K. Li, “A reputation-based resilient and recoverable P2P botnet,” in IEEE Second International Conference on Data Science in Cyberspace (DSC) , 2017, pp. 275–282
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
W. Deng, M.-J. Lai, Z. Peng, and W. Yin, “Parallel multi-block ADMM with o(1/k) convergence,” Journal of Scientific Computing , vol. 71, no. 2, pp. 712–736, 2017
2017
Earlier work this paper cites.
D. Alistarh, D. Grubic, J. Li, R. Tomioka, and M. Vojnovic, “QSGD: Communication-efficient SGD via gradient quantization and encoding,” in Advances in Neural Information Processing Systems , I. Guyon et al. , Eds., vol. 30. Curran Associates, Inc., 2017
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
X. Lian, C. Zhang, H. Zhang, C.-J. Hsieh, W. Zhang, and J. Liu, “Can decentralized algorithms outperform centralized algorithms? a case study for decentralized parallel stochastic gradient descent,” in 31st International Conference on Neural Information Processing Systems , ser. NIPS’17. Curran Associates Inc., 2017, p. 5336–5346
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
L. He, A. Bian, and M. Jaggi, “COLA: Decentralized Linear Learning,” in 32nd International Conference on Neural Information Processing Systems , ser. NIPS’18. Curran Associates Inc., 2018, p. 4541–4551
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
H. Tang, S. Gan, C. Zhang, T. Zhang, and J. Liu, “Communication compression for decentralized training,” in 32nd International Conference on Neural Information Processing Systems , ser. NIPS’18. Curran Associates Inc., 2018, p. 7663–7673
2018
Earlier work this paper cites.
R. Anil, G. Pereyra, A. T. Passos, R. Ormandi, G. Dahl, and G. Hinton, “Large scale distributed neural network training through online distillation,” in 6th International Conference on Learning Representations, ICLR 2018 , 2018
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
D. Liu, T. Miller, R. Sayeed, and K. D. Mandl, “FADL: Federated-autonomous deep learning for distributed electronic health record,” 2018
2018
Earlier work this paper cites.
T. J. Pollard, A. E. W. Johnson, J. D. Raffa, L. A. Celi, R. G. Mark, and O. Badawi, “The eICU collaborative research database, a freely available multi-center database for critical care research,” Scientific Data , vol. 5, no. 1, p. 180178, 2018
2018
Earlier work this paper cites.
P. Tschandl, C. Rosendahl, and H. Kittler, “The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions,” Scientific Data , vol. 5, no. 1, p. 180161, 2018
2018
Earlier work this paper cites.
Y. Meidan et al. , “N-BaIoT: Network-Based Detection of IoT Botnet Attacks Using Deep Autoencoders,” IEEE Pervasive Computing , vol. 17, no. 3, pp. 12–22, 2018
2018
Earlier work this paper cites.
K. Bonawitz et al. , “Towards federated learning at scale: System design,” in Proceedings of Machine Learning and Systems , A. Talwalkar, V. Smith, and M. Zaharia, Eds., vol. 1, 2019, pp. 374–388
2019
Earlier work this paper cites.
A. Koloskova, S. U. Stich, and M. Jaggi, “Decentralized stochastic optimization and gossip algorithms with compressed communication,” in 36th International Conference on Machine Learning , vol. 97. PMLR, 2019, pp. 3479–3487
2019
Earlier work this paper cites.
D. Basu, D. Data, C. Karakus, and S. Diggavi, Qsparse-Local-SGD: Distributed SGD with Quantization, Sparsification, and Local Computations . Curran Associates Inc., 2019
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
L. Huang, A. L. Shea, H. Qian, A. Masurkar, H. Deng, and D. Liu, “Patient clustering improves efficiency of federated machine learning to predict mortality and hospital stay time using distributed electronic medical records,” Journal of Biomedical Informatics , vol. 99, p. 103291, 2019
2019
Earlier work this paper cites.
X. Mao, Y. Gu, and W. Yin, “Walk proximal gradient: An energy-efficient algorithm for consensus optimization,” IEEE Internet of Things Journal , vol. 6, no. 2, pp. 2048–2060, 2019
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
X. Wang, Y. Peng, L. Lu, Z. Lu, M. Bagheri, and R. M. Summers, ChestX-ray: Hospital-Scale Chest X-ray Database and Benchmarks on Weakly Supervised Classification and Localization of Common Thorax Diseases . Springer International Publishing, 2019, pp. 369–392
2019
Earlier work this paper cites.
R. Fisher, “Cvonline: Image databases,” 2019. [Online]. Available: https://homepages.inf.ed.ac.uk/rbf/CVonline/Imagedbase.htm
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
Z. Yu, J. Hu, G. Min, H. Xu, and J. Mills, “Proactive content caching for internet-of-vehicles based on peer-to-peer federated learning,” in IEEE 26th International Conference on Parallel and Distributed Systems , 2020, pp. 601–608
2020
Earlier work this paper cites.
W. Y. B. Lim et al. , “Federated learning in mobile edge networks: A comprehensive survey,” IEEE Communications Surveys & Tutorials , vol. 22, no. 3, pp. 2031–2063, 2020
2020
Earlier work this paper cites.
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
Earlier work this paper cites.
O. Marfoq, C. Xu, G. Neglia, and R. Vidal, “Throughput-optimal topology design for cross-silo federated learning,” in 34th International Conference on Neural Information Processing Systems , ser. NIPS’20. Curran Associates Inc., 2020
2020
Earlier work this paper cites.
C. Briggs, Z. Fan, and P. Andras, “Federated learning with hierarchical clustering of local updates to improve training on non-iid data,” in International Joint Conference on Neural Networks , 2020, pp. 1–9
2020
Earlier work this paper cites.
J. Jiang, L. Hu, C. Hu, J. Liu, and Z. Wang, “BACombo: Bandwidth-aware decentralized federated learning,” Electronics , vol. 9, no. 3, 2020
2020
Earlier work this paper cites.
Y. Chen, F. Luo, T. Li, T. Xiang, Z. Liu, and J. Li, “A training-integrity privacy-preserving federated learning scheme with trusted execution environment,” Information Sciences , vol. 522, pp. 69–79, 2020
2020
Earlier work this paper cites.
Y. Qu et al. , “Decentralized privacy using blockchain-enabled federated learning in fog computing,” IEEE Internet of Things Journal , vol. 7, no. 6, pp. 5171–5183, 2020
2020
Earlier work this paper cites.
J. Jiang and L. Hu, “Decentralised federated learning with adaptive partial gradient aggregation,” CAAI Transactions on Intelligence Technology , vol. 5, no. 3, pp. 230–236, 2020
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
K. Hsieh, A. Phanishayee, O. Mutlu, and P. B. Gibbons, “The non-iid data quagmire of decentralized machine learning,” in 37th International Conference on Machine Learning , ser. ICML’20. JMLR.org, 2020
2020
Earlier work this paper cites.
Y. Ye, H. Chen, Z. Ma, and M. Xiao, “Decentralized consensus optimization based on parallel random walk,” IEEE Communications Letters , vol. 24, no. 2, pp. 391–395, 2020
2020
Earlier work this paper cites.
X. Mao, K. Yuan, Y. Hu, Y. Gu, A. H. Sayed, and W. Yin, “Walkman: A communication-efficient random-walk algorithm for decentralized optimization,” IEEE Transactions on Signal Processing , vol. 68, pp. 2513–2528, 2020
2020
Earlier work this paper cites.
C. He et al. , “FedML: A research library and benchmark for federated machine learning,” Advances in Neural Information Processing Systems, Best Paper Award at Federate Learning Workshop , 2020
2020
Earlier work this paper cites.
S. Lu, Y. Zhang, and Y. Wang, “Decentralized federated learning for electronic health records,” in 54th Annual Conference on Information Sciences and Systems , 2020, pp. 1–5
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
S. Savazzi, “Federated learning: mmwave mimo radar dataset for testing,” IEEE Dataport , 2020
2020
Earlier work this paper cites.
S. Garcia, A. Parmisano, and M. J. Erquiaga, “IoT-23: A labeled dataset with malicious and benign IoT network traffic,” 2020
2020
Earlier work this paper cites.
A. Alsaedi, N. Moustafa, Z. Tari, A. Mahmood, and A. Anwar, “TON_IoT Telemetry Dataset: A New Generation Dataset of IoT and IIoT for Data-Driven Intrusion Detection Systems,” IEEE Access , vol. 8, pp. 165 130–165 150, 2020
2020
Earlier work this paper cites.
R. Damasevicius et al. , “LITNET-2020: An annotated real-world network flow dataset for network intrusion detection,” Electronics , vol. 9, no. 5, 2020
2020
Earlier work this paper cites.
B. Rozemberczki and R. Sarkar, “Characteristic functions on graphs: Birds of a feather, from statistical descriptors to parametric models,” in Association for Computing Machinery , 2020, p. 1325–1334
2020
Earlier work this paper cites.
N. I. Mowla, N. H. Tran, I. Doh, and K. Chae, “Federated learning-based cognitive detection of jamming attack in flying ad-hoc network,” IEEE Access , vol. 8, pp. 4338–4350, 2020
2020
Cited alongside, same era.
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
Cited alongside, same era.
Y. Lu, X. Huang, K. Zhang, S. Maharjan, and Y. Zhang, “Blockchain empowered asynchronous federated learning for secure data sharing in Internet of Vehicles,” IEEE Transactions on Vehicular Technology , vol. 69, no. 4, pp. 4298–4311, 2020
2020
Cited alongside, same era.
S. R. Pokhrel and J. Choi, “Federated learning with blockchain for autonomous vehicles: Analysis and design challenges,” IEEE Transactions on Communications , vol. 68, no. 8, pp. 4734–4746, 2020
2020
Cited alongside, same era.
P. Pinyoanuntapong, W. H. Huff, M. Lee, C. Chen, and P. Wang, “Toward scalable and robust AIoT via decentralized federated learning,” IEEE Internet of Things Magazine , vol. 5, no. 1, pp. 30–35, 2022
2022
Closest in time.
2022
Closest in time.
Z. Chen, W. Liao, P. Tian, Q. Wang , and W. Yu, “A fairness-aware peer-to-peer decentralized learning framework with heterogeneous devices,” Future Internet , vol. 14, no. 5, 2022
2022
Closest in time.
2022
Closest in time.
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A. Hard, K. Partridge, R. Mathews, and S. Augenstein, “Jointly learning from decentralized (federated) and centralized data to mitigate distribution shift,” in Proceedings of NeurIPS Workshop on Distribution Shifts , 2021
2021
Cited alongside, same era.
T. Wang, Y. Liu, X. Zheng, H.-N. Dai, W. Jia, and M. Xie, “Edge-based communication optimization for distributed federated learning,” IEEE Transactions on Network Science and Engineering , pp. 1–1, 2021
2021
Cited alongside, same era.
S. Savazzi, M. Nicoli, M. Bennis, S. Kianoush, and L. Barbieri, “Opportunities of federated learning in connected, cooperative, and automated industrial systems,” IEEE Communications Magazine , vol. 59, no. 2, pp. 16–21, 2021
2021
Cited alongside, same era.
Y. Wang, Z. Su, N. Zhang, and A. Benslimane, “Learning in the air: Secure federated learning for uav-assisted crowdsensing,” IEEE Transactions on Network Science and Engineering , vol. 8, no. 2, pp. 1055–1069, 2021
2021
Cited alongside, same era.
J.-H. Chen, M.-R. Chen, G.-Q. Zeng, and J.-S. Weng, “BDFL: A byzantine-fault-tolerance decentralized federated learning method for autonomous vehicle,” IEEE Transactions on Vehicular Technology , vol. 70, no. 9, pp. 8639–8652, 2021
2021
Cited alongside, same era.
D. C. Nguyen, M. Ding, P. N. Pathirana, A. Seneviratne, J. Li, and H. V. Poor, “Federated learning for Internet of Things: A comprehensive survey,” IEEE Communications Surveys & Tutorials , vol. 23, no. 3, pp. 1622–1658, 2021
2021
Cited alongside, same era.
L. U. Khan, W. Saad, Z. Han, E. Hossain, and C. S. Hong, “Federated learning for Internet of Things: Recent advances, taxonomy, and open challenges,” IEEE Communications Surveys & Tutorials , vol. 23, no. 3, pp. 1759–1799, 2021
2021
Cited alongside, same era.
V. Mothukuri, R. M. Parizi, S. Pouriyeh, Y. Huang, A. Dehghantanha, and G. Srivastava, “A survey on security and privacy of federated learning,” Future Generation Computer Systems , vol. 115, pp. 619–640, 2021
2021
Cited alongside, same era.
X. Wang, A. Lalitha, T. Javidi, and F. Koushanfar, “Peer-to-peer variational federated learning over arbitrary graphs,” IEEE Journal on Selected Areas in Information Theory , pp. 1–1, 2022
2022
Closest in time.
2022
Closest in time.
M. Qi, Z. Wang, S. Chen, and Y. Xiang, “A hybrid incentive mechanism for decentralized federated learning,” Distrib. Ledger Technol. , 2022
2022
Closest in time.
Y. Belal, A. Bellet, S. B. Mokhtar, and V. Nitu, “PEPPER: Empowering user-centric recommender systems over gossip learning,” Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. , vol. 6, no. 3, 2022
2022
Closest in time.
M. Khelghatdoust and M. Mahdavi, “A socially-aware, privacy-preserving, and scalable federated learning protocol for distributed online social networks,” in Advanced Information Networking and Applications . Springer International Publishing, 2022, pp. 192–203
2022
Closest in time.
B. Niu, Y. Chen, Z. Wang, F. Li, B. Wang, and H. Li, “Eclipse: Preserving differential location privacy against long-term observation attacks,” IEEE Transactions on Mobile Computing , vol. 21, no. 1, pp. 125–138, 2022
2022
Closest in time.
S. Chen, D. Yu, Y. Zou, J. Yu, and X. Cheng, “Decentralized wireless federated learning with differential privacy,” IEEE Transactions on Industrial Informatics , vol. 18, no. 9, pp. 6273–6282, 2022
2022
Closest in time.
H. Ye, L. Liang, and G. Y. Li, “Decentralized federated learning with unreliable communications,” IEEE Journal of Selected Topics in Signal Processing , vol. 16, no. 3, pp. 487–500, 2022
2022
Closest in time.
T.-T. Kuo and A. Pham, “Detecting model misconducts in decentralized healthcare federated learning,” International Journal of Medical Informatics , vol. 158, p. 104658, 2022
2022
Closest in time.
A. Gholami, N. Torkzaban, and J. S. Baras, “Trusted decentralized federated learning,” in IEEE 19th Annual Consumer Communications & Networking Conference , 2022, pp. 1–6
2022
Closest in time.
V. Mothukuri, R. M. Parizi, S. Pouriyeh, A. Dehghantanha, and K.-K. R. Choo, “FabricFL: Blockchain-in-the-loop federated learning for trusted decentralized systems,” IEEE Systems Journal , vol. 16, no. 3, pp. 3711–3722, 2022
2022
Closest in time.
B. Camajori Tedeschini et al. , “Decentralized federated learning for healthcare networks: A case study on tumor segmentation,” IEEE Access , vol. 10, pp. 8693–8708, 2022
2022
Closest in time.
W. Liu, L. Chen, and W. Zhang, “Decentralized federated learning: Balancing communication and computing costs,” IEEE Transactions on Signal and Information Processing over Networks , vol. 8, pp. 131–143, 2022
2022
Closest in time.
S. Wu, D. Huang, and H. Wang, “Network gradient descent algorithm for decentralized federated learning,” Journal of Business & Economic Statistics , pp. 1–13, 2022
2022
Closest in time.
X. Li, Y. Li, J. Wang, C. Chen, L. Yang, and Z. Zheng, “Decentralized federated meta-learning framework for few-shot multitask learning,” International Journal of Intelligent Systems , vol. 37, no. 11, pp. 8490–8522, 2022
2022
Closest in time.
C. Wu, F. Wu, L. Lyu, Y. Huang, and X. Xie, “Communication-efficient federated learning via knowledge distillation,” Nature Communications , vol. 13, no. 1, p. 2032, 2022
2022
Closest in time.
I. Shiri et al. , “Decentralized distributed multi-institutional pet image segmentation using a federated deep learning framework,” Clinical Nuclear Medicine , vol. 47, no. 7, 2022
2022
Closest in time.
J. Wang, X. Chang, R. J. Rodrìguez, and Y. Wang, “Assessing anonymous and selfish free-rider attacks in federated learning,” in IEEE Symposium on Computers and Communications , 2022, pp. 1–6
2022
Closest in time.
Z. Chen, D. Li, J. Zhu, and S. Zhang, “DACFL: Dynamic average consensus-based federated learning in decentralized sensors network,” Sensors , vol. 22, no. 9, 2022
2022
Closest in time.
D. C. Nguyen, S. Hosseinalipour, D. J. Love, P. N. Pathirana, and C. G. Brinton, “Latency optimization for blockchain-empowered federated learning in multi-server edge computing,” IEEE Journal on Selected Areas in Communications , vol. 40, no. 12, pp. 3373–3390, 2022
2022
Closest in time.
Google, “Tensorflow federated,” TensorFlow, 2022. [Online]. Available: https://www.tensorflow.org/federated
2022
Closest in time.
2022
Closest in time.
J. Li et al. , “Blockchain assisted decentralized federated learning (BLADE-FL): Performance analysis and resource allocation,” IEEE Transactions on Parallel and Distributed Systems , vol. 33, no. 10, pp. 2401–2415, 2022
2022
Closest in time.
C. Che, X. Li, C. Chen, X. He, and Z. Zheng, “A decentralized federated learning framework via committee mechanism with convergence guarantee,” IEEE Transactions on Parallel and Distributed Systems , vol. 33, no. 12, pp. 4783–4800, 2022
2022
Closest in time.
Q. Liu et al. , “Asynchronous decentralized federated learning for collaborative fault diagnosis of pv stations,” IEEE Transactions on Network Science and Engineering , vol. 9, no. 3, pp. 1680–1696, 2022
2022
Closest in time.
2022
Closest in time.
Z. Lian and C. Su, “Decentralized federated learning for Internet of Things anomaly detection,” in ACM on Asia Conference on Computer and Communications Security . Association for Computing Machinery, 2022, p. 1249–1251
2022
Closest in time.
M. Abdel-Basset, N. Moustafa, and H. Hawash, “Privacy-preserved cyberattack detection in Industrial Edge of Things (IEoT): A blockchain-orchestrated federated learning approach,” IEEE Transactions on Industrial Informatics , vol. 18, no. 11, pp. 7920–7934, 2022
2022
Closest in time.
2022
Closest in time.
2022
Closest in time.
C. Zhu, X. Zhu, J. Ren, and T. Qin, “Blockchain-enabled federated learning for UAV edge computing network: Issues and solutions,” IEEE Access , vol. 10, pp. 56 591–56 610, 2022
2022
Closest in time.
C. Reiser, “creiser/drone-detection,” GitHub, 2022. [Online]. Available: https://github.com/creiser/drone-detection
2022
Closest in time.
E. T. Martínez Beltrán, M. Quiles Pérez, S. López Bernal, G. Martínez Pérez, and A. Huertas Celdrán, “SAFECAR: A brain–computer interface and intelligent framework to detect drivers’ distractions,” Expert Systems with Applications , vol. 203, p. 117402, 2022
2022
Closest in time.
2023
Closest in time.
2023
Closest in time.
L. Witt, M. Heyer, K. Toyoda, W. Samek, and D. Li, “Decentral and incentivized federated learning frameworks: A systematic literature review,” IEEE Internet of Things Journal , vol. 10, no. 4, pp. 3642–3663, 2023
2023
Closest in time.
T. Ma, H. Wang, and C. Li, “Quantized distributed federated learning for industrial internet of things,” IEEE Internet of Things Journal , vol. 10, no. 4, pp. 3027–3036, 2023
2023
Closest in time.
M. S. Al-Abiad, M. Obeed, M. J. Hossain, and A. Chaaban, “Decentralized aggregation for energy-efficient federated learning via d2d communications,” IEEE Transactions on Communications , vol. 71, no. 6, pp. 3333–3351, 2023
2023
Closest in time.
Z. Tang, S. Shi, B. Li, and X. Chu, “Gossipfl: A decentralized federated learning framework with sparsified and adaptive communication,” IEEE Transactions on Parallel and Distributed Systems , vol. 34, no. 3, pp. 909–922, 2023
2023
Closest in time.
G. Lu, Z. Xiong, R. Li, and W. Li, “Decentralized federated learning: A defense against gradient inversion attack,” in Wireless Internet , 2023, pp. 44–56
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
L. Wang, X. Zhao, Z. Lu, L. Wang, and S. Zhang, “Enhancing privacy preservation and trustworthiness for decentralized federated learning,” Information Sciences , vol. 628, pp. 449–468, 2023
2023
Closest in time.
L. Barbieri, S. Savazzi, and M. Nicoli, “A layer selection optimizer for communication-efficient decentralized federated deep learning,” IEEE Access , vol. 11, pp. 22 155–22 173, 2023
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
H. Gao, M. T. Thai, and J. Wu, “When decentralized optimization meets federated learning,” IEEE Network , vol. In press, pp. 1–7, 2023
2023
Closest in time.
2023
Closest in time.
N. Rodríguez-Barroso, D. Jiménez-López, M. V. Luzón, F. Herrera, and E. Martínez-Cámara, “Survey on federated learning threats: Concepts, taxonomy on attacks and defences, experimental study and challenges,” Information Fusion , vol. 90, pp. 148–173, 2023
2023
Closest in time.
X. Ma and D. Xu, “Torr: A lightweight blockchain for decentralized federated learning,” IEEE Internet of Things Journal , vol. In press, pp. 1–1, 2023
2023
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T. Sun, D. Li, and B. Wang, “Decentralized federated averaging,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 45, no. 4, pp. 4289–4301, 2023
2023
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Y. Yuan et al. , “Decefl: a principled fully decentralized federated learning framework,” National Science Open , vol. 2, no. 1, p. 20220043, 2023
2023
Closest in time.
S. K. Singh, L. T. Yang, and J. H. Park, “Fusionfedblock: Fusion of blockchain and federated learning to preserve privacy in industry 5.0,” Information Fusion , vol. 90, pp. 233–240, 2023
2023
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2023
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2023
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2023
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X. Zhou et al. , “Decentralized p2p federated learning for privacy-preserving and resilient mobile robotic systems,” IEEE Wireless Communications , vol. 30, no. 2, pp. 82–89, 2023
2023
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2023
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M. M. Salim and J. H. Park, “Federated learning-based secure electronic health record sharing scheme in medical informatics,” IEEE Journal of Biomedical and Health Informatics , vol. 27, no. 2, pp. 617–624, 2023
2023
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Y. Tian, S. Wang, J. Xiong, R. Bi, Z. Zhou, and M. Z. A. Bhuiyan, “Robust and privacy-preserving decentralized deep federated learning training: Focusing on digital healthcare applications,” IEEE/ACM Transactions on Computational Biology and Bioinformatics , vol. In press, pp. 1–12, 2023
2023
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T. Ranathunga, A. McGibney, S. Rea, and S. Bharti, “Blockchain-based decentralized model aggregation for cross-silo federated learning in industry 4.0,” IEEE Internet of Things Journal , vol. 10, no. 5, pp. 4449–4461, 2023
2023
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A. Salama, A. Stergioulis, A. M. Hayajneh, S. A. R. Zaidi, D. McLernon, and I. Robertson, “Decentralized federated learning over slotted aloha wireless mesh networking,” IEEE Access , vol. 11, pp. 18 326–18 342, 2023
2023
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A. Giannopoulos, P. Gkonis, P. Bithas, N. Nomikos, G. Ntroulias, and P. Trakadas, “Federated learning for maritime environments:use cases, experimental results, and open issues,” TechRxiv preprint techrxiv.22133549.v1 , 2023
2023
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X. Hu, R. Li, Y. Ning, K. Ota, and L. Wang, “A data sharing scheme based on federated learning in iov,” IEEE Transactions on Vehicular Technology , vol. In press, pp. 1–13, 2023
2023
Closest in time.