Fetching the paper…

Reading the bibliography…

2021

Real-time End-to-End Federated Learning: An Automotive Case Study

Zhang, Hongyi, Bosch, Jan, Olsson, Helena Holmström

Original
PDF
Post on X

Understand

With the development and the increasing interests in ML/DL fields, companies are eager to apply Machine Learning/Deep Learning approaches to increase service quality and customer experience.

Federated Learning was implemented as an effective model training method for distributing and accelerating time-consuming model training while protecting user data privacy.
However, common Federated Learning approaches, on the other hand, use a synchronous protocol to conduct model aggregation, which is inflexible and unable to adapt to rapidly changing environments and heterogeneous hardware settings in real-world scenarios.
In this paper, we present an approach to real-time end-to-end Federated Learning combined with a novel asynchronous model aggregation protocol.

Real-time End-to-End Federated Learning: An Automotive Case Study · Around

Built on

B. K. Horn and B. G. Schunck, “Determining optical flow,” in Techniques and Applications of Image Understanding , vol. 281. International Society for Optics and Photonics, 1981, pp. 319–331
1981
Earlier work this paper cites.
D. Pomerleau, “An autonomous land vehicle in a neural network,” Advances in neural information processing systems’(Morgan Kaufmann Publishers Inc., 1989) , vol. 1, 1998
1998
Earlier work this paper cites.
D. Zhang and J. J. Tsai, “Machine learning and software engineering,” Software Quality Journal , vol. 11, no. 2, pp. 87–119, 2003
2003
Earlier work this paper cites.
G. Farnebäck, “Two-frame motion estimation based on polynomial expansion,” in Scandinavian conference on Image analysis . Springer, 2003, pp. 363–370
2003
Earlier work this paper cites.
M. T. Beck, M. Werner, S. Feld, and S. Schimper, “Mobile edge computing: A taxonomy,” in Proc. of the Sixth International Conference on Advances in Future Internet . Citeseer, 2014, pp. 48–55
2014
Earlier work this paper cites.
K. Simonyan and A. Zisserman, “Two-stream convolutional networks for action recognition in videos,” in Advances in neural information processing systems , 2014, pp. 568–576
2014
Earlier work this paper cites.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980 , 2014
Original
2014
Earlier work this paper cites.
T. Park, N. Abuzainab, and W. Saad, “Learning how to communicate in the internet of things: Finite resources and heterogeneity,” arXiv preprint arXiv:1610.01586 , 2016
Original
2016
Earlier work this paper cites.
J. Konečnỳ, H. B. McMahan, D. Ramage, and P. Richtárik, “Federated optimization: Distributed machine learning for on-device intelligence,” arXiv preprint arXiv:1610.02527 , 2016
Original
2016
Earlier work this paper cites.
M. Bojarski, D. Del Testa, D. Dworakowski, B. Firner, B. Flepp, P. Goyal, L. D. Jackel, M. Monfort, U. Muller, J. Zhang et al. , “End to end learning for self-driving cars,” arXiv preprint arXiv:1604.07316 , 2016
Original
2016
Earlier work this paper cites.

Similar

J. Konečnỳ, H. B. McMahan, F. X. Yu, P. Richtárik, A. T. Suresh, and D. Bacon, “Federated learning: Strategies for improving communication efficiency,” arXiv preprint arXiv:1610.05492 , 2016
Original
2016
Cited alongside, same era.
A. L’heureux, K. Grolinger, H. F. Elyamany, and M. A. Capretz, “Machine learning with big data: Challenges and approaches,” IEEE Access , vol. 5, pp. 7776–7797, 2017
2017
Cited alongside, same era.
H. M. Eraqi, M. N. Moustafa, and J. Honer, “End-to-end deep learning for steering autonomous vehicles considering temporal dependencies,” arXiv preprint arXiv:1710.03804 , 2017
Original
2017
Cited alongside, same era.
J. Janai, F. Güney, A. Behl, and A. Geiger, “Computer vision for autonomous vehicles: Problems, datasets and state-of-the-art,” arXiv preprint arXiv:1704.05519 , 2017
Original
2017
Cited alongside, same era.

Then

L. E. Lwakatare, A. Raj, J. Bosch, H. H. Olsson, and I. Crnkovic, “A taxonomy of software engineering challenges for machine learning systems: An empirical investigation,” in International Conference on Agile Software Development . Springer, Cham, 2019, pp. 227–243
2019
Later among the works it cites.
S. Ramaswamy, R. Mathews, K. Rao, and F. Beaufays, “Federated learning for emoji prediction in a mobile keyboard,” arXiv preprint arXiv:1906.04329 , 2019
Original
2019
Later among the works it cites.
S. Du, H. Guo, and A. Simpson, “Self-driving car steering angle prediction based on image recognition,” arXiv preprint arXiv:1912.05440 , 2019
Original
2019
Later among the works it cites.
R. Valiente, M. Zaman, S. Ozer, and Y. P. Fallah, “Controlling steering angle for cooperative self-driving vehicles utilizing cnn and lstm-based deep networks,” in 2019 IEEE Intelligent Vehicles Symposium (IV) . IEEE, 2019, pp. 2423–2428
2019

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

P. J. Navarro, C. Fernandez, R. Borraz, and D. Alonso, “A machine learning approach to pedestrian detection for autonomous vehicles using high-definition 3d range data,” Sensors , vol. 17, no. 1, p. 18, 2017

2017

Cited alongside, same era.

A. Hard, K. Rao, R. Mathews, S. Ramaswamy, F. Beaufays, S. Augenstein, H. Eichner, C. Kiddon, and D. Ramage, “Federated learning for mobile keyboard prediction,” arXiv preprint arXiv:1811.03604 , 2018

2018

Cited alongside, same era.

N. Fernandez, “Two-stream convolutional networks for end-to-end learning of self-driving cars,” arXiv preprint arXiv:1811.05785 , 2018

2018

Cited alongside, same era.

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

Cited alongside, same era.

SullyChen. (2018) Collection of labeled car driving datasets. [Online]. Available: https://github.com/SullyChen/driving-datasets

2018

Cited alongside, same era.

X. Li, K. Huang, W. Yang, S. Wang, and Z. Zhang, “On the convergence of fedavg on non-iid data,” arXiv preprint arXiv:1907.02189 , 2019

2019

Cited alongside, same era.

Later among the works it cites.

S. Lu, Y. Yao, and W. Shi, “Collaborative learning on the edges: A case study on connected vehicles,” in 2nd { \{ USENIX } \} Workshop on Hot Topics in Edge Computing (HotEdge 19) , 2019

2019

Later among the works it cites.

Y. M. Saputra, D. T. Hoang, D. N. Nguyen, E. Dutkiewicz, M. D. Mueck, and S. Srikanteswara, “Energy demand prediction with federated learning for electric vehicle networks,” arXiv preprint arXiv:1909.00907 , 2019

2019

Later among the works it 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

Later among the works it cites.

J. Bosch, I. Crnkovic, and H. H. Olsson, “Engineering ai systems: A research agenda,” arXiv preprint arXiv:2001.07522 , 2020

2020

Later among the works it cites.

F. U. Haq, D. Shin, S. Nejati, and L. C. Briand, “Comparing offline and online testing of deep neural networks: An autonomous car case study,” in 2020 IEEE 13th International Conference on Software Testing, Validation and Verification (ICST) . IEEE, 2020, pp. 85–95

2020

Later among the works it cites.

S. Doomra, N. Kohli, and S. Athavale, “Turn signal prediction: A federated learning case study,” arXiv preprint arXiv:2012.12401 , 2020

2020

Later among the works it cites.