Fetching the paper…
Reading the bibliography…
In recent years, mobile devices are equipped with increasingly advanced sensing and computing capabilities.
B. S. Kashin, “Diameters of some finite-dimensional sets and classes of smooth functions,” Izvestiya Rossiiskoi Akademii Nauk. Seriya Matematicheskaya , vol. 41, no. 2, pp. 334–351, 1977
1977
Earlier work this paper cites.
H. Bourlard and Y. Kamp, “Auto-association by multilayer perceptrons and singular value decomposition,” Biological cybernetics , vol. 59, no. 4-5, pp. 291–294, 1988
1988
Earlier work this paper cites.
R. H. Myers and R. H. Myers, Classical and modern regression with applications . Duxbury press Belmont, CA, 1990, vol. 2
1990
Earlier work this paper cites.
R. Hecht-Nielsen, “Theory of the backpropagation neural network,” in Neural networks for perception . Elsevier, 1992, pp. 65–93
1992
Earlier work this paper cites.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation , vol. 9, no. 8, pp. 1735–1780, 1997
1997
Earlier work this paper cites.
C. J. Burges, “A tutorial on support vector machines for pattern recognition,” Data mining and knowledge discovery , vol. 2, no. 2, pp. 121–167, 1998
1998
Earlier work this paper cites.
Y. LeCun, L. Bottou, Y. Bengio, P. Haffner et al. , “Gradient-based learning applied to document recognition,” Proceedings of the IEEE , vol. 86, no. 11, pp. 2278–2324, 1998
1998
Earlier work this paper cites.
Y. Rubner, C. Tomasi, and L. J. Guibas, “The earth mover’s distance as a metric for image retrieval,” International journal of computer vision , vol. 40, no. 2, pp. 99–121, 2000
2000
Earlier work this paper cites.
P. Bradley, K. Bennett, and A. Demiriz, “Constrained k-means clustering,” Microsoft Research, Redmond , vol. 20, no. 0, p. 0, 2000
2000
Earlier work this paper cites.
F. Lau, S. H. Rubin, M. H. Smith, and L. Trajkovic, “Distributed denial of service attacks,” in Smc 2000 conference proceedings. 2000 ieee international conference on systems, man and cybernetics.’cybernetics evolving to systems, humans, organizations, and their complex interactions’(cat. no. 0 , vol. 3. IEEE, 2000, pp. 2275–2280
2000
Earlier work this paper cites.
J. C. Bezdek and R. J. Hathaway, “Convergence of alternating optimization,” Neural, Parallel & Scientific Computations , vol. 11, no. 4, pp. 351–368, 2003
2003
Earlier work this paper cites.
R. Jurca and B. Faltings, “An incentive compatible reputation mechanism,” in EEE International Conference on E-Commerce, 2003. CEC 2003. IEEE, 2003, pp. 285–292
2003
Earlier work this paper cites.
M. Sviridenko, “A note on maximizing a submodular set function subject to a knapsack constraint,” Operations Research Letters , vol. 32, no. 1, pp. 41–43, 2004
2004
Earlier work this paper cites.
S. Boyd and L. Vandenberghe, Convex optimization . Cambridge university press, 2004
2004
Earlier work this paper cites.
M. J. Osborne et al. , An introduction to game theory . Oxford university press New York, 2004, vol. 3, no. 3
2004
Earlier work this paper cites.
X. J. Zhu, “Semi-supervised learning literature survey,” University of Wisconsin-Madison Department of Computer Sciences, Tech. Rep., 2005
2005
Earlier work this paper cites.
P. D. Tao et al. , “The dc (difference of convex functions) programming and dca revisited with dc models of real world nonconvex optimization problems,” Annals of operations research , vol. 133, no. 1-4, pp. 23–46, 2005
2005
Earlier work this paper cites.
P. Bolton, M. Dewatripont et al. , Contract theory . MIT press, 2005
2005
Earlier work this paper cites.
C. Dwork, F. McSherry, K. Nissim, and A. Smith, “Calibrating noise to sensitivity in private data analysis,” in Theory of cryptography conference . Springer, 2006, pp. 265–284
2006
Earlier work this paper cites.
W. Xu, K. Ma, W. Trappe, and Y. Zhang, “Jamming sensor networks: attack and defense strategies,” IEEE network , vol. 20, no. 3, pp. 41–47, 2006
2006
Earlier work this paper cites.
Z.-Q. Luo, N. D. Sidiropoulos, P. Tseng, and S. Zhang, “Approximation bounds for quadratic optimization with homogeneous quadratic constraints,” SIAM Journal on optimization , vol. 18, no. 1, pp. 1–28, 2007
2007
Earlier work this paper cites.
A. Asuncion and D. Newman, “Uci machine learning repository,” 2007
2007
Earlier work this paper cites.
L. De Haan and A. Ferreira, Extreme value theory: an introduction . Springer Science & Business Media, 2007
2007
Earlier work this paper cites.
M. J. Neely, E. Modiano, and C.-P. Li, “Fairness and optimal stochastic control for heterogeneous networks,” IEEE/ACM Transactions On Networking , vol. 16, no. 2, pp. 396–409, 2008
2008
Earlier work this paper cites.
M. Strasser, C. Popper, S. Capkun, and M. Cagalj, “Jamming-resistant key establishment using uncoordinated frequency hopping,” in 2008 IEEE Symposium on Security and Privacy (sp 2008) . IEEE, 2008, pp. 64–78
2008
Earlier work this paper cites.
A. Krizhevsky, G. Hinton et al. , “Learning multiple layers of features from tiny images,” Citeseer, Tech. Rep., 2009
2009
Earlier work this paper cites.
M. Buscema and S. Terzi, “Semeion handwritten digit data set,” Center for Machine Learning and Intelligent Systems, California, USA , 2009
2009
Earlier work this paper cites.
D. López-Pérez, A. Valcarce, G. De La Roche, and J. Zhang, “Ofdma femtocells: A roadmap on interference avoidance,” IEEE Communications Magazine , vol. 47, no. 9, pp. 41–48, 2009
2009
Earlier work this paper cites.
S. J. Pan and Q. Yang, “A survey on transfer learning,” IEEE Transactions on knowledge and data engineering , vol. 22, no. 10, pp. 1345–1359, 2009
2009
Earlier work this paper cites.
S. Priya and D. J. Inman, Energy harvesting technologies . Springer, 2009, vol. 21
2009
Earlier work this paper cites.
T. Mikolov, M. Karafiát, L. Burget, J. Černockỳ, and S. Khudanpur, “Recurrent neural network based language model,” in Eleventh annual conference of the international speech communication association , 2010
2010
Earlier work this paper cites.
Y. LeCun, C. Cortes, and C. Burges, “Mnist handwritten digit database,” AT&T Labs [Online]. Available: http://yann. lecun. com/exdb/mnist , vol. 2, p. 18, 2010
2010
Earlier work this paper cites.
J. Chung, H.-J. Yoon, and H. J. Gardner, “Analysis of break in presence during game play using a linear mixed model,” ETRI journal , vol. 32, no. 5, pp. 687–694, 2010
2010
Earlier work this paper cites.
M. J. Neely, “Stochastic network optimization with application to communication and queueing systems,” Synthesis Lectures on Communication Networks , vol. 3, no. 1, pp. 1–211, 2010
2010
Earlier work this paper cites.
I. I. Eliazar and I. M. Sokolov, “Measuring statistical heterogeneity: The pietra index,” Physica A: Statistical Mechanics and its Applications , vol. 389, no. 1, pp. 117–125, 2010
2010
Earlier work this paper cites.
R. K. Ganti, F. Ye, and H. Lei, “Mobile crowdsensing: current state and future challenges,” IEEE Communications Magazine , vol. 49, no. 11, pp. 32–39, 2011
2011
Earlier work this paper cites.
J. M. Joyce, “Kullback-leibler divergence,” International encyclopedia of statistical science , pp. 720–722, 2011
2011
Earlier work this paper cites.
J. Bloemer, “How to share a secret,” Communications of the Acm , vol. 22, no. 22, pp. 612–613, 2011
2011
Earlier work this paper cites.
B. Nazer and M. Gastpar, “Compute-and-forward: Harnessing interference through structured codes,” IEEE Transactions on Information Theory , vol. 57, no. 10, pp. 6463–6486, 2011
2011
Earlier work this paper cites.
O. Chapelle and L. Li, “An empirical evaluation of thompson sampling,” in Advances in neural information processing systems , 2011, pp. 2249–2257
2011
Earlier work this paper cites.
O. Guéant, J.-M. Lasry, and P.-L. Lions, “Mean field games and applications,” in Paris-Princeton lectures on mathematical finance 2010 . Springer, 2011, pp. 205–266
2011
Earlier work this paper cites.
G. Hinton, N. Srivastava, and K. Swersky, “Neural networks for machine learning lecture 6a overview of mini-batch gradient descent,” Cited on , vol. 14, p. 8, 2012
2012
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in neural information processing systems , 2012, pp. 1097–1105
2012
Earlier work this paper cites.
K. Kumar, J. Liu, Y.-H. Lu, and B. Bhargava, “A survey of computation offloading for mobile systems,” Mobile Networks and Applications , vol. 18, no. 1, pp. 129–140, 2013
2013
Earlier work this paper cites.
2013
Earlier work this paper cites.
M. Long, J. Wang, G. Ding, J. Sun, and P. S. Yu, “Transfer feature learning with joint distribution adaptation,” in Proceedings of the IEEE international conference on computer vision , 2013, pp. 2200–2207
2013
Earlier work this paper cites.
D. Anguita, A. Ghio, L. Oneto, X. Parra, and J. L. Reyes-Ortiz, “A public domain dataset for human activity recognition using smartphones.” in Esann , 2013
2013
Earlier work this paper cites.
W. He, G. Yan, and L. Da Xu, “Developing vehicular data cloud services in the iot environment,” IEEE Transactions on Industrial Informatics , vol. 10, no. 2, pp. 1587–1595, 2014
2014
Earlier work this paper cites.
B. M. Gaff, H. E. Sussman, and J. Geetter, “Privacy and big data,” Computer , vol. 47, no. 6, pp. 7–9, 2014
2014
Earlier work this paper cites.
C. Dong, C. C. Loy, K. He, and X. Tang, “Learning a deep convolutional network for image super-resolution,” in European conference on computer vision . Springer, 2014, pp. 184–199
2014
Earlier work this paper cites.
X.-W. Chen and X. Lin, “Big data deep learning: challenges and perspectives,” IEEE access , vol. 2, pp. 514–525, 2014
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
M. Jaggi, V. Smith, M. Takác, J. Terhorst, S. Krishnan, T. Hofmann, and M. I. Jordan, “Communication-efficient distributed dual coordinate ascent,” in Advances in neural information processing systems , 2014, pp. 3068–3076
2014
Earlier work this paper cites.
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from overfitting,” The journal of machine learning research , vol. 15, no. 1, pp. 1929–1958, 2014
2014
Earlier work this paper cites.
H.-W. Ng and S. Winkler, “A data-driven approach to cleaning large face datasets,” in 2014 IEEE International Conference on Image Processing (ICIP) . IEEE, 2014, pp. 343–347
2014
Earlier work this paper cites.
P. Laskov et al. , “Practical evasion of a learning-based classifier: A case study,” in 2014 IEEE symposium on security and privacy . IEEE, 2014, pp. 197–211
2014
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Advances in neural information processing systems , 2014, pp. 2672–2680
2014
Earlier work this paper cites.
M. Gerla, E.-K. Lee, G. Pau, and U. Lee, “Internet of vehicles: From intelligent grid to autonomous cars and vehicular clouds,” in 2014 IEEE world forum on internet of things (WF-IoT) . IEEE, 2014, pp. 241–246
2014
Earlier work this paper cites.
P. You and Z. Yang, “Efficient optimal scheduling of charging station with multiple electric vehicles via v2v,” in 2014 IEEE International Conference on Smart Grid Communications (SmartGridComm) . IEEE, 2014, pp. 716–721
2014
Earlier work this paper cites.
J.-S. Leu, T.-H. Chiang, M.-C. Yu, and K.-W. Su, “Energy efficient clustering scheme for prolonging the lifetime of wireless sensor network with isolated nodes,” IEEE communications letters , vol. 19, no. 2, pp. 259–262, 2014
2014
Earlier work this paper cites.
R. Pryss, M. Reichert, J. Herrmann, B. Langguth, and W. Schlee, “Mobile crowd sensing in clinical and psychological trials–a case study,” in 2015 IEEE 28th International Symposium on Computer-Based Medical Systems . IEEE, 2015, pp. 23–24
2015
Earlier work this paper cites.
Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” nature , vol. 521, no. 7553, p. 436, 2015
2015
Earlier work this paper cites.
D. Oletic and V. Bilas, “Design of sensor node for air quality crowdsensing,” in 2015 IEEE Sensors Applications Symposium (SAS) . IEEE, 2015, pp. 1–5
2015
Earlier work this paper cites.
X. Chen, L. Jiao, W. Li, and X. Fu, “Efficient multi-user computation offloading for mobile-edge cloud computing,” IEEE/ACM Transactions on Networking , vol. 24, no. 5, pp. 2795–2808, 2015
2015
Earlier work this paper cites.
J. Schmidhuber, “Deep learning in neural networks: An overview,” Neural networks , vol. 61, pp. 85–117, 2015
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
M. Long, Y. Cao, J. Wang, and M. I. Jordan, “Learning transferable features with deep adaptation networks,” in Proceedings of the 32nd International Conference on International Conference on Machine Learning-Volume 37 . JMLR. org, 2015, pp. 97–105
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
N. Strom, “Scalable distributed dnn training using commodity gpu cloud computing,” in Sixteenth Annual Conference of the International Speech Communication Association , 2015
2015
Earlier work this paper cites.
M. Feldman, “Computational fairness: Preventing machine-learned discrimination,” 2015
2015
Earlier work this paper cites.
R. Dennis and G. Owen, “Rep on the block: A next generation reputation system based on the blockchain,” in 2015 10th International Conference for Internet Technology and Secured Transactions (ICITST) . IEEE, 2015, pp. 131–138
2015
Cited alongside, same era.
G. Ateniese, L. V. Mancini, A. Spognardi, A. Villani, D. Vitali, and G. Felici, “Hacking smart machines with smarter ones: How to extract meaningful data from machine learning classifiers,” International Journal of Security , vol. 10, no. 3, pp. 137–150, 2015
2015
Cited alongside, same era.
M. Fredrikson, S. Jha, and T. Ristenpart, “Model inversion attacks that exploit confidence information and basic countermeasures,” in Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security . ACM, 2015, pp. 1322–1333
2015
Cited alongside, same era.
R. Shokri and V. Shmatikov, “Privacy-preserving deep learning,” in Proceedings of the 22nd ACM SIGSAC conference on computer and communications security . ACM, 2015, pp. 1310–1321
2018
Later among the works it cites.
Z. Tao and Q. Li, “esgd: Communication efficient distributed deep learning on the edge,” in { \{ USENIX } \} Workshop on Hot Topics in Edge Computing (HotEdge 18) , 2018
2018
Later among the works it cites.
X. Yao, C. Huang, and L. Sun, “Two-stream federated learning: Reduce the communication costs,” in 2018 IEEE Visual Communications and Image Processing (VCIP) . IEEE, 2018, pp. 1–4
2018
Later among the works it cites.
M. R. Sprague, A. Jalalirad, M. Scavuzzo, C. Capota, M. Neun, L. Do, and M. Kopp, “Asynchronous federated learning for geospatial applications,” in Joint European Conference on Machine Learning and Knowledge Discovery in Databases . Springer, 2018, pp. 21–28
2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2015
Cited alongside, same era.
N. Moustafa and J. Slay, “Unsw-nb15: a comprehensive data set for network intrusion detection systems (unsw-nb15 network data set),” in 2015 military communications and information systems conference (MilCIS) . IEEE, 2015, pp. 1–6
2015
Cited alongside, same era.
H.-J. Hong, C.-L. Fan, Y.-C. Lin, and C.-H. Hsu, “Optimizing cloud-based video crowdsensing,” IEEE Internet of Things Journal , vol. 3, no. 3, pp. 299–313, 2016
2016
Cited alongside, same era.
W. Shi, J. Cao, Q. Zhang, Y. Li, and L. Xu, “Edge computing: Vision and challenges,” IEEE Internet of Things Journal , vol. 3, no. 5, pp. 637–646, 2016
2016
Cited alongside, same era.
M. Abadi, A. Chu, I. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, and L. Zhang, “Deep learning with differential privacy,” in Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security . ACM, 2016, pp. 308–318
2016
Cited alongside, same era.
H. B. McMahan, E. Moore, D. Ramage, and B. A. y Arcas, “Federated learning of deep networks using model averaging,” 2016
2016
Cited alongside, same era.
2016
Cited alongside, same era.
G. Trigeorgis, F. Ringeval, R. Brueckner, E. Marchi, M. A. Nicolaou, B. Schuller, and S. Zafeiriou, “Adieu features? end-to-end speech emotion recognition using a deep convolutional recurrent network,” in 2016 IEEE international conference on acoustics, speech and signal processing (ICASSP) . IEEE, 2016, pp. 5200–5204
2016
Cited alongside, same era.
S. C. Wong, A. Gatt, V. Stamatescu, and M. D. McDonnell, “Understanding data augmentation for classification: when to warp?” in 2016 international conference on digital image computing: techniques and applications (DICTA) . IEEE, 2016, pp. 1–6
2016
Cited alongside, same era.
2018
Later among the works it cites.
2018
Later among the works it cites.
2018
Later among the works it cites.
2018
Later among the works it cites.
2018
Later among the works it cites.
2018
Later among the works it cites.
2018
Later among the works it cites.
2018
Later among the works it cites.
J. Weng, J. Weng, J. Zhang, M. Li, Y. Zhang, and W. Luo, “Deepchain: Auditable and privacy-preserving deep learning with blockchain-based incentive,” Cryptology ePrint Archive, Report 2018/679 , 2018
2018
Later among the works it cites.
A. Abeshu and N. Chilamkurti, “Deep learning: the frontier for distributed attack detection in fog-to-things computing,” IEEE Communications Magazine , vol. 56, no. 2, pp. 169–175, 2018
2018
Later among the works it cites.
2018
Later among the works it cites.
D. Preuveneers, V. Rimmer, I. Tsingenopoulos, J. Spooren, W. Joosen, and E. Ilie-Zudor, “Chained anomaly detection models for federated learning: An intrusion detection case study,” Applied Sciences , vol. 8, no. 12, p. 2663, 2018
2018
Later among the works it cites.
Z. Yu, J. Hu, G. Min, H. Lu, Z. Zhao, H. Wang, and N. Georgalas, “Federated learning based proactive content caching in edge computing,” in 2018 IEEE Global Communications Conference (GLOBECOM) . IEEE, 2018, pp. 1–6
2018
Later among the works it cites.
2018
Later among the works it cites.
S. Samarakoon, M. Bennis, W. Saad, and M. Debbah, “Federated learning for ultra-reliable low-latency v2v communications,” in 2018 IEEE Global Communications Conference (GLOBECOM) . IEEE, 2018, pp. 1–7
2018
Later among the works it cites.
K. K. Nguyen, D. T. Hoang, D. Niyato, P. Wang, D. Nguyen, and E. Dutkiewicz, “Cyberattack detection in mobile cloud computing: A deep learning approach,” in 2018 IEEE Wireless Communications and Networking Conference (WCNC) . IEEE, 2018, pp. 1–6
2018
Later among the works it cites.
W. Li, T. Logenthiran, V.-T. Phan, and W. L. Woo, “Implemented iot-based self-learning home management system (shms) for singapore,” IEEE Internet of Things Journal , vol. 5, no. 3, pp. 2212–2219, 2018
2018
Later among the works it cites.
R. Boutaba, M. A. Salahuddin, N. Limam, S. Ayoubi, N. Shahriar, F. Estrada-Solano, and O. M. Caicedo, “A comprehensive survey on machine learning for networking: evolution, applications and research opportunities,” Journal of Internet Services and Applications , vol. 9, no. 1, p. 16, 2018
2018
Later among the works it cites.
2018
Later among the works it cites.
B. Custers, A. Sears, F. Dechesne, I. Georgieva, T. Tani, and S. van der Hof, EU Personal Data Protection in Policy and Practice . Springer, 2019
2019
Closest in time.
2019
Closest in time.
K. Powell, “Nvidia clara federated learning to deliver ai to hospitals while protecting patient data,” https://blogs.nvidia.com/blog/2019/12/01/clara-federated-learning/ , 2019
2019
Closest in time.
C. Zhang, P. Patras, and H. Haddadi, “Deep learning in mobile and wireless networking: A survey,” IEEE Communications Surveys & Tutorials , 2019
2019
Closest in time.
N. C. Luong, D. T. Hoang, S. Gong, D. Niyato, P. Wang, Y.-C. Liang, and D. I. Kim, “Applications of deep reinforcement learning in communications and networking: A survey,” IEEE Communications Surveys & Tutorials , 2019
2019
Closest in time.
Q. Yang, Y. Liu, T. Chen, and Y. Tong, “Federated machine learning: Concept and applications,” ACM Transactions on Intelligent Systems and Technology (TIST) , vol. 10, no. 2, p. 12, 2019
2019
Closest in time.
2019
Closest in time.
2019
Closest in time.
2019
Closest in time.
J. Yao, T. Han, and N. Ansari, “On mobile edge caching,” IEEE Communications Surveys & Tutorials , vol. 21, no. 3, pp. 2525–2553, 2019
2019
Closest in time.
J. Wang, Y. Chen, S. Hao, X. Peng, and L. Hu, “Deep learning for sensor-based activity recognition: A survey,” Pattern Recognition Letters , vol. 119, pp. 3–11, 2019
2019
Closest in time.
J. Kang, Z. Xiong, D. Niyato, S. Xie, and J. Zhang, “Incentive mechanism for reliable federated learning: A joint optimization approach to combining reputation and contract theory,” 09 2019
2019
Closest in time.
S. Wang, T. Tuor, T. Salonidis, K. K. Leung, C. Makaya, T. He, and K. Chan, “Adaptive federated learning in resource constrained edge computing systems,” IEEE Journal on Selected Areas in Communications , 2019
2019
Closest in time.
2019
Closest in time.
2019
Closest in time.
2019
Closest in time.
2019
Closest in time.
2019
Closest in time.
2019
Closest in time.
2019
Closest in time.
M. I. Jordan, J. D. Lee, and Y. Yang, “Communication-efficient distributed statistical inference,” Journal of the American Statistical Association , vol. 114, no. 526, pp. 668–681, 2019
2019
Closest in time.
2019
Closest in time.
2019
Closest in time.
2019
Closest in time.
2019
Closest in time.
2019
Closest in time.
2019
Closest in time.
2019
Closest in time.
2019
Closest in time.
2019
Closest in time.
L. Melis, C. Song, E. De Cristofaro, and V. Shmatikov, “Exploiting unintended feature leakage in collaborative learning.” IEEE, 2019
2019
Closest in time.
2019
Closest in time.
A. Triastcyn and B. Faltings, “Federated generative privacy,” arXiv preprint arXiv:1910.08385 , 2019
2019
Closest in time.
M. Hao, H. Li, G. Xu, S. Liu, and H. Yang, “Towards efficient and privacy-preseving federated deep learning,” in 2019 IEEE International Conference on Communications . IEEE, 2019, pp. 1–6
2019
Closest in time.
J. Ren, H. Wang, T. Hou, S. Zheng, and C. Tang, “Federated learning-based computation offloading optimization in edge computing-supported internet of things,” IEEE Access , vol. 7, pp. 69 194–69 201, 2019
2019
Closest in time.
Y. Qian, L. Hu, J. Chen, X. Guan, M. M. Hassan, and A. Alelaiwi, “Privacy-aware service placement for mobile edge computing via federated learning,” Information Sciences , vol. 505, pp. 562–570, 2019
2019
Closest in time.
Y. Saputra, H. Dinh, D. Nguyen, E. Dutkiewicz, M. Mueck, and S. Srikanteswara, “Energy demand prediction with federated learning for electric vehicle networks,” in IEEE Global Communications Conference (GLOBECOM) , 2019
2019
Closest in time.
L. Jiang, R. Tan, X. Lou, and G. Lin, “On lightweight privacy-preserving collaborative learning for internet-of-things objects,” 2019
2019
Closest in time.
Z. Gu, H. Jamjoom, D. Su, H. Huang, J. Zhang, T. Ma, D. Pendarakis, and I. Molloy, “Reaching data confidentiality and model accountability on the caltrain,” in 2019 49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) . IEEE, 2019, pp. 336–348
2019
Closest in time.
2019
Closest in time.
2020
Closest in time.
Y. Zhan, P. Li, Z. Qu, D. Zeng, and S. Guo, “A learning-based incentive mechanism for federated learning,” IEEE Internet of Things Journal , 2020
2020
Closest in time.
D. Ye, R. Yu, M. Pan, and Z. Han, “Federated learning in vehicular edge computing: A selective model aggregation approach,” IEEE Access , 2020
2020
Closest in time.
H. Yu, Z. Liu, Y. Liu, T. Chen, M. Cong, X. Weng, D. Niyato, and Q. Yang, “A fairness-aware incentive scheme for federated learning,” AIES 20: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society , 2020
2020
Closest in time.
2020
Closest in time.
Cisco, “Cisco global cloud index: Forecast and methodology, 2016Ð2021 white paper.”
2021
Closest in time.