Federated Learning of Deep Neural Decision Forests

Sjöberg, Anders; Gustavsson, Emil; Koppisetty, Ashok Chaitanya; Jirstrand, Mats

doi:10.1007/978-3-030-37599-7_58

Cited by 8 publications

(5 citation statements)

References 11 publications

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“…Neural DTs do however not include automatic feature selection which can lead to worse performance. In their study, [28] demonstrated how Neural DTs can be combined with FL for the MNIST data set. In collaboration with Volvo, [12] extended the work on FL using Neural DTs and showed how it can be applied in a vehicular setting for BDD100k data set.…”

Section: Related Workmentioning

confidence: 99%

Federated Learning for Tabular Data using TabNet: A Vehicular Use-Case

Lindskog

Prehofer

2022

2022 IEEE 18th International Conference on Intelligent Computer Communication and Processing (ICCP)

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In this paper, we show how Federated Learning (FL) can be applied to vehicular use-cases in which we seek to classify obstacles, irregularities and pavement types on roads. Our proposed framework utilizes FL and TabNet, a state-ofthe-art neural network for tabular data. We are the first to demonstrate how TabNet can be integrated with FL. Moreover, we achieve a maximum test accuracy of 93.6%. Finally, we reason why FL is a suitable concept for this data set.

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Section: Related Workmentioning

confidence: 99%

Federated Learning for Tabular Data using TabNet: A Vehicular Use-Case

Lindskog

Prehofer

2022

2022 IEEE 18th International Conference on Intelligent Computer Communication and Processing (ICCP)

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“…We furthermore have used OODIDA, or derivatives of it, for research in machine learning. This includes a performance evaluation of our own implementations of various federated learning algorithms [15] as well as ongoing work on federated learning of neural decision forests [19].…”

Section: Related Workmentioning

confidence: 99%

OODIDA: On-Board/Off-Board Distributed Real-Time Data Analytics for Connected Vehicles

et al. 2021

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A fleet of connected vehicles easily produces many gigabytes of data per hour, making centralized (off-board) data processing impractical. In addition, there is the issue of distributing tasks to on-board units in vehicles and processing them efficiently. Our solution to this problem is On-board/Off-board Distributed Data Analytics (OODIDA), which is a platform that tackles both task distribution to connected vehicles as well as concurrent execution of tasks on arbitrary subsets of edge clients. Its message-passing infrastructure has been implemented in Erlang/OTP, while the end points use a language-independent JSON interface. Computations can be carried out in arbitrary programming languages. The message-passing infrastructure of OODIDA is highly scalable, facilitating the execution of large numbers of concurrent tasks.

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Section: Related Workmentioning

confidence: 99%

OODIDA: On-board/Off-board Distributed Real-Time Data Analytics for Connected Vehicles

Ulm,

Smith,

Nilsson

et al. 2019

Preprint

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A fleet of connected vehicles easily produces many gigabytes of data per hour, making centralized (off-board) data processing impractical. In addition, there is the issue of distributing tasks to on-board units in vehicles and processing them efficiently. Our solution to this problem is OODIDA (On-board/Off-board Distributed Data Analytics), which is a platform that tackles both task distribution to connected vehicles as well as concurrent execution of tasks on arbitrary subsets of edge clients. Its message-passing infrastructure has been implemented in Erlang/OTP, while the end points use a language-independent JSON interface. Computations can be carried out in arbitrary programming languages. The messagepassing infrastructure of OODIDA is highly scalable, facilitating the execution of large numbers of concurrent tasks.

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Federated Learning of Deep Neural Decision Forests

Cited by 8 publications

References 11 publications

Federated Learning for Tabular Data using TabNet: A Vehicular Use-Case

Federated Learning for Tabular Data using TabNet: A Vehicular Use-Case

OODIDA: On-Board/Off-Board Distributed Real-Time Data Analytics for Connected Vehicles

OODIDA: On-board/Off-board Distributed Real-Time Data Analytics for Connected Vehicles

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