![](bg8.png)
with smaller datasets will have more benefits from the
accurate prediction model. A particularly beneficial
case could be if we suppose that each taxi’s dataset
is confidential and should be processed locally (self-
interested ride providers) as in (Ramanan et al., 2020).
Despite the fact, that the proposed explainable
federated approach does not depend on the explain-
ability method, only one explainability method (Inte-
grated gradients) was analysed. We plan to compare
different explainability methods and their combina-
tions as our future work. We believe that this ap-
proach could be successfully implemented for more
complex prediction and classification tasks with more
complex deep learning and federated learning archi-
tectures. For example, we aim to implement explain-
able federated learning for traffic demand forecasting
models.
ACKNOWLEDGEMENTS
The research was funded by the Lower Saxony Min-
istry of Science and Culture under grant number
ZN3493 within the Lower Saxony “Vorab“ of the
Volkswagen Foundation and supported by the Center
for Digital Innovations (ZDIN).
REFERENCES
Al-Abbasi, A. O., Ghosh, A., and Aggarwal, V. (2019).
Deeppool: Distributed model-free algorithm for ride-
sharing using deep reinforcement learning. IEEE
Transactions on ITSs, 20(12):4714–4727.
Ancona, M., Ceolini, E.,
¨
Oztireli, C., and Gross, M. (2018).
Towards better understanding of gradient-based attri-
bution methods for deep neural networks. In In Proc.
of 6th Int. Conf. on Learning Representations, ICLR.
Bonawitz, K., Eichner, H., Grieskamp, W., Huba, D., Inger-
man, A., Ivanov, V., Kiddon, C., Konecn
´
y, J., Maz-
zocchi, S., McMahan, H. B., Overveldt, T. V., Petrou,
D., Ramage, D., and Roselander, J. (2019). Towards
federated learning at scale: System design. CoRR,
abs/1902.01046.
Ciskowski, P., Drzewinski, G., Bazan, M., and Janiczek, T.
(2018). Estimation of travel time in the city using neu-
ral networks trained with simulated urban traffic data.
In DepCoS-RELCOMEX 2018, volume 761 of Ad-
vances in Intelligent Systems and Computing, pages
121–134. Springer.
Dotoli, M., Zgaya, H., Russo, C., and Hammadi, S. (2017).
A multi-agent advanced traveler information system
for optimal trip planning in a co-modal framework.
IEEE Transactions on Intelligent Transportation Sys-
tems, 18(9):2397–2412.
Fiosina, J. and Fiosins, M. (2014). Resampling based mod-
elling of individual routing preferences in a distributed
traffic network. Int. Journal of AI, 12(1):79–103.
Fiosina, J. and Fiosins, M. (2017). Distributed nonpara-
metric and semiparametric regression on spark for big
data forecasting. Applied Comp. Int. and Soft Com-
puting, 2017:13.
Fiosina, J., Fiosins, M., and Bonn, S. (2019). Explainable
deep learning for augmentation of sRNA expression
profiles. Journal of Computational Biology. (to ap-
pear).
Fiosina, J., Fiosins, M., and M
¨
uller, J. (2013a). Big data
processing and mining for next generation intelligent
transportation systems. Jurnal Teknologi, 63(3).
Fiosina, J., Fiosins, M., and M
¨
uller, J. (2013b). Decen-
tralised cooperative agent-based clustering in intelli-
gent traffic clouds. In Proc. of 11th German Conf. on
MAS Technologies, volume 8076 of LNAI, pages 59–
72. Springer.
Holzinger, A. (2018). Explainable AI (ex-AI). Informatik
Spektrum, 41:138–143.
Khair, Y., Dennai, A., and Elmir, Y. (2021). A survey on
cloud-based intelligent transportation system, icaires
2020. In AI and Renewables Towards an Energy Tran-
sition, volume 174 of LNNS. Springer.
Konecn
´
y, J., McMahan, H. B., Ramage, D., and Richt
´
arik,
P. (2016). Federated optimization: Distributed ma-
chine learning for on-device intelligence. CoRR,
abs/1610.02527.
Kraus, S., Azaria, A., Fiosina, J., Greve, M., Hazon, N.,
Kolbe, L., Lembcke, T., M
¨
uller, J., Schleibaum, S.,
and Vollrath, M. (2020). AI for explaining decisions in
multi-agent environment. In AAAI-2020. AAAI Press.
Molnar, C. (2020). Interpretable Machine Learning. A
Guide for Making Black Box Models Explainable.
Lulu.com.
Ramanan, P., Nakayama, K., and Sharma, R. (2020). Baffle:
Blockchain based aggregator free federated learning.
CoRR, abs/1909.07452.
Ryffel, T., Trask, A., Dahl, M., Wagner, B., Mancuso, J.,
Rueckert, D., and Passerat-Palmbach, J. (2018). A
generic framework for privacy preserving deep learn-
ing. CoRR, abs/1811.04017.
Shrikumar, A., Greenside, P., and Kundaje, A. (2017).
Learning important features through propagating ac-
tivation differences. In Proc. of ML Research, vol-
ume 70, pages 3145–3153, Int. Convention Centre,
Sydney, Australia. PMLR.
Sundararajan, M., Taly, A., and Yan, Q. (2017). Ax-
iomatic attribution for deep networks. In Proc. of the
34th Int. Conf. on ML, volume 70 of ICML’17, page
3319–3328. JMLR.org.
Wang, G. (2019). Interpret federated learning with shapley
values. CoRR, abs/1905.04519.
Yang, Q., Liu, Y., Chen, T., and Tong, Y. (2019). Federated
machine learning: Concept and applications. ACM
Trans. Intell. Syst. Technol., 10(2).
Explainable Federated Learning for Taxi Travel Time Prediction
677