Fetching the paper…
Reading the bibliography…
A new federated learning (FL) framework enabled by large-scale wireless connectivity is proposed for designing the autonomous controller of connected and autonomous vehicles (CAVs).
S. Boyd and L. Vandenberghe,
2004
Earlier work this paper cites.
S. Boyd and L. Vandenberghe,
2004
Earlier work this paper cites.
P. Bolton, M. Dewatripont
2005
Earlier work this paper cites.
P. Bolton, M. Dewatripont
2005
Earlier work this paper cites.
G. Acosta-Marum and M. Ingram, “Doubly selective vehicle-to-vehicle channel measurements and modeling at 5.9 GHz,” in
2006
Earlier work this paper cites.
G. Acosta-Marum and M. Ingram, “Doubly selective vehicle-to-vehicle channel measurements and modeling at 5.9 GHz,” in
2006
Earlier work this paper cites.
Q. Song, J. C. Spall, Y. C. Soh, and J. Ni, “Robust neural network tracking controller using simultaneous perturbation stochastic approximation,”
2008
Earlier work this paper cites.
Q. Song, J. C. Spall, Y. C. Soh, and J. Ni, “Robust neural network tracking controller using simultaneous perturbation stochastic approximation,”
2008
Earlier work this paper cites.
L. dos Santos Coelho and D. L. de Andrade Bernert, “An improved harmony search algorithm for synchronization of discrete-time chaotic systems,”
2009
Earlier work this paper cites.
L. dos Santos Coelho and D. L. de Andrade Bernert, “An improved harmony search algorithm for synchronization of discrete-time chaotic systems,”
2009
Earlier work this paper cites.
M. Kamal, M. Mukai, J. Murata, and T. Kawabe, “Ecological vehicle control on roads with up-down slopes,” IEEE Transactions on Intelligent Transportation Systems , vol. 12, no. 3, pp. 783–794, Sept. 2011
2011
Earlier work this paper cites.
M. Kamal, M. Mukai, J. Murata, and T. Kawabe, “Ecological vehicle control on roads with up-down slopes,” IEEE Transactions on Intelligent Transportation Systems , vol. 12, no. 3, pp. 783–794, Sept. 2011
2011
Earlier work this paper cites.
S. S. Ge, C. C. Hang, T. H. Lee, and T. Zhang,
2013
Earlier work this paper cites.
G. Tagne, R. Talj, and A. Charara, “Higher-order sliding mode control for lateral dynamics of autonomous vehicles, with experimental validation,” in
2013
Earlier work this paper cites.
S. S. Ge, C. C. Hang, T. H. Lee, and T. Zhang,
2013
Earlier work this paper cites.
G. Tagne, R. Talj, and A. Charara, “Higher-order sliding mode control for lateral dynamics of autonomous vehicles, with experimental validation,” in
2013
Earlier work this paper cites.
X. Liu and P. Lu, “Solving nonconvex optimal control problems by convex optimization,”
2014
Earlier work this paper cites.
X. Liu and P. Lu, “Solving nonconvex optimal control problems by convex optimization,”
2014
Earlier work this paper cites.
J. Kong, M. Pfeiffer, G. Schildbach, and F. Borrelli, “Kinematic and dynamic vehicle models for autonomous driving control design,” in Proc. of IEEE Intelligent Vehicles Symposium , Seoul, South Korea, Jun. 2015
2015
Earlier work this paper cites.
K. Nam, Y. Hori, and C. Lee, “Wheel slip control for improving traction-ability and energy efficiency of a personal electric vehicle,” Energies , vol. 8, no. 7, pp. 6820–6840, Jul. 2015
2015
Earlier work this paper cites.
T. Gu, J. Atwood, C. Dong, J. M. Dolan, and J.-W. Lee, “Tunable and stable real-time trajectory planning for urban autonomous driving,” in
2015
Earlier work this paper cites.
J. Kong, M. Pfeiffer, G. Schildbach, and F. Borrelli, “Kinematic and dynamic vehicle models for autonomous driving control design,” in Proc. of IEEE Intelligent Vehicles Symposium , Seoul, South Korea, Jun. 2015
2015
Earlier work this paper cites.
K. Nam, Y. Hori, and C. Lee, “Wheel slip control for improving traction-ability and energy efficiency of a personal electric vehicle,” Energies , vol. 8, no. 7, pp. 6820–6840, Jul. 2015
2015
Earlier work this paper cites.
T. Gu, J. Atwood, C. Dong, J. M. Dolan, and J.-W. Lee, “Tunable and stable real-time trajectory planning for urban autonomous driving,” in
2015
Earlier work this paper cites.
B. Paden, M. Čáp, S. Z. Yong, D. Yershov, and E. Frazzoli, “A survey of motion planning and control techniques for self-driving urban vehicles,” IEEE Transactions on Intelligent Vehicles , vol. 1, no. 1, pp. 33–55, Mar. 2016
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
B. Paden, M. Čáp, S. Z. Yong, D. Yershov, and E. Frazzoli, “A survey of motion planning and control techniques for self-driving urban vehicles,” IEEE Transactions on Intelligent Vehicles , vol. 1, no. 1, pp. 33–55, Mar. 2016
2016
Cited alongside, same era.
2016
Cited alongside, same era.
B. Chen, Y. Xu, and A. Shrivastava, “Fast and accurate stochastic gradient estimation,” in
2016
Cited alongside, same era.
G. Han, W. Fu, W. Wang, and Z. Wu, “The lateral tracking control for the intelligent vehicle based on adaptive PID neural network,”
2017
Cited alongside, same era.
V. Smith, C.-K. Chiang, M. Sanjabi, and A. Talwalkar, “Federated multi-task learning,” in
2017
S. Xiong, H. Xie, K. Song, and G. Zhang, “A speed tracking method for autonomous driving via ADRC with extended state observer,”
2019
Later among the works it cites.
L. Hewing, K. P. Wabersich, M. Menner, and M. N. Zeilinger, “Learning-based model predictive control: Toward safe learning in control,” Annual Review of Control, Robotics, and Autonomous Systems , vol. 3, no. 1, pp. 269–296, May 2020
2020
Later among the works it cites.
H. Shiri, J. Park, and M. Bennis, “Communication-efficient massive UAV online path control: Federated learning meets mean-field game theory,”
2020
Later among the works it cites.
Y. Zhan, P. Li, Z. Qu, D. Zeng, and S. Guo, “A learning-based incentive mechanism for federated learning,”
2020
Later among the works it cites.
L. U. Khan, S. R. Pandey, N. H. Tran, W. Saad, Z. Han, M. N. H. Nguyen, and C. S. Hong, “Federated learning for edge networks: Resource optimization and incentive mechanism,”
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
S. Moosavi, B. Tehrani, and R. Ramnath, “Trajectory annotation by discovering driving patterns,” in
2017
Cited alongside, same era.
G. Han, W. Fu, W. Wang, and Z. Wu, “The lateral tracking control for the intelligent vehicle based on adaptive PID neural network,”
2017
Cited alongside, same era.
V. Smith, C.-K. Chiang, M. Sanjabi, and A. Talwalkar, “Federated multi-task learning,” in
2017
Cited alongside, same era.
S. Moosavi, B. Tehrani, and R. Ramnath, “Trajectory annotation by discovering driving patterns,” in
2017
Cited alongside, same era.
L. Nie, J. Guan, C. Lu, H. Zheng, and Z. Yin, “Longitudinal speed control of autonomous vehicle based on a self-adaptive PID of radial basis function neural network,” IET Intelligent Transport Systems , vol. 12, no. 6, pp. 485–494, Mar. 2018
2018
Cited alongside, same era.
S. C. Lin, Y. Zhang, C. H. Hsu, M. Skach, M. E. Haque, L. Tang, and J. Mars, “The architectural implications of autonomous driving: Constraints and acceleration,” in
2018
Cited alongside, same era.
L. Bottou, F. Curtis, and J. Nocedal, “Optimization methods for large-scale machine learning,”
2018
Cited alongside, same era.
2020
Later among the works it cites.
D. Ye, R. Yu, M. Pan, and Z. Han, “Federated learning in vehicular edge computing: A selective model aggregation approach,”
2020
Later among the works it cites.
W. Y. B. Lim, Z. Xiong, C. Miao, D. Niyato, Q. Yang, C. Leung, and H. V. Poor, “Hierarchical incentive mechanism design for federated machine learning in mobile networks,”
2020
Later among the works it cites.
F. Yu, H. Chen, X. Wang, W. Xian, Y. Chen, F. Liu, V. Madhavan, and T. Darrell, “BDD100K: A diverse driving dataset for heterogeneous multitask learning,” in
2020
Later among the works it cites.
M. Chen, H. V. Poor, W. Saad, and S. Cui, “Wireless communications for collaborative federated learning,”
2020
Later among the works it cites.
T. Li, A. K. Sahu, M. Zaheer, M. Sanjabi, A. Talwalkar, and V. Smith, “Federated optimization in heterogeneous networks,” in
2020
Later among the works it cites.
L. Hewing, K. P. Wabersich, M. Menner, and M. N. Zeilinger, “Learning-based model predictive control: Toward safe learning in control,” Annual Review of Control, Robotics, and Autonomous Systems , vol. 3, no. 1, pp. 269–296, May 2020
2020
Later among the works it cites.
H. Shiri, J. Park, and M. Bennis, “Communication-efficient massive UAV online path control: Federated learning meets mean-field game theory,”
2020
Later among the works it cites.
Y. Zhan, P. Li, Z. Qu, D. Zeng, and S. Guo, “A learning-based incentive mechanism for federated learning,”
2020
Later among the works it cites.
L. U. Khan, S. R. Pandey, N. H. Tran, W. Saad, Z. Han, M. N. H. Nguyen, and C. S. Hong, “Federated learning for edge networks: Resource optimization and incentive mechanism,”
2020
Later among the works it cites.
D. Ye, R. Yu, M. Pan, and Z. Han, “Federated learning in vehicular edge computing: A selective model aggregation approach,”
2020
Later among the works it cites.
W. Y. B. Lim, Z. Xiong, C. Miao, D. Niyato, Q. Yang, C. Leung, and H. V. Poor, “Hierarchical incentive mechanism design for federated machine learning in mobile networks,”
2020
Later among the works it cites.
F. Yu, H. Chen, X. Wang, W. Xian, Y. Chen, F. Liu, V. Madhavan, and T. Darrell, “BDD100K: A diverse driving dataset for heterogeneous multitask learning,” in
2020
Later among the works it cites.
M. Chen, H. V. Poor, W. Saad, and S. Cui, “Wireless communications for collaborative federated learning,”
2020
Later among the works it cites.
T. Li, A. K. Sahu, M. Zaheer, M. Sanjabi, A. Talwalkar, and V. Smith, “Federated optimization in heterogeneous networks,” in
2020
Later among the works it cites.
T. Zeng, O. Semiari, M. Chen, W. Saad, and M. Bennis, “Federated learning for autonomous controller design in connected and autonomous vehicles,” in Proc. of IEEE Conference on Decision and Control (CDC) , Austin, TX, USA, Dec. 2021
2021
Closest in time.
M. Chen, Z. Yang, W. Saad, C. Yin, H. V. Poor, and S. Cui, “A joint learning and communications framework for federated learning over wireless networks,”
2021
Closest in time.
T. Zeng, O. Semiari, M. Chen, W. Saad, and M. Bennis, “Federated learning for autonomous controller design in connected and autonomous vehicles,” in Proc. of IEEE Conference on Decision and Control (CDC) , Austin, TX, USA, Dec. 2021
2021
Closest in time.
M. Chen, Z. Yang, W. Saad, C. Yin, H. V. Poor, and S. Cui, “A joint learning and communications framework for federated learning over wireless networks,”
2021
Closest in time.
M. Kim, W. Saad, M. Mozaffari, and M. Debbah, “On the tradeoff between energy, precision, and accuracy in federated quantized neural networks,” in
2022
Closest in time.
M. Kim, W. Saad, M. Mozaffari, and M. Debbah, “On the tradeoff between energy, precision, and accuracy in federated quantized neural networks,” in
2022
Closest in time.