Author Archives: Gregor Ulm

New Preprint: OODIDA: On-board/Off-board Distributed Data Analytics for Connected Vehicles

A preprint of our paper “OODIDA: On-board/Off-board Distributed Data Analytics for Connected Vehicles” is now available on arXiv. It describes a distributed system for data analytics for the automotive industry, targeting a fleet of reference vehicles. The abstract is reproduced below:

OODIDA: On-board/Off-board Distributed Data Analytics
for Connected Vehicles
Gregor Ulm, Emil Gustavsson, and Mats Jirstrand

Connected vehicles may produce gigabytes of data per
hour, which makes centralized data processing
impractical at the fleet level. In addition, there
are the problems of distributing tasks to edge
devices 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 large-scale tasks
on arbitrary subsets of clients. Its message-passing
infrastructure has been implemented in Erlang/OTP,
while the end points are language-agnostic. OODIDA is
highly scalable and able to process a significant
volume of data on resource-constrained clients.

Paper “Functional Federated Learning in Erlang (ffl-erl)” accepted for publication

Our paper “Functional Federated Learning in Erlang (fflerl)” has been accepted for publication in the Proceedings of the 26th International Workshop on Functional and (Constraint) Logic Programming (WFLP 2018). These proceedings will appear as Springer Lecture Notes in Computer Science Vol. 11285.

The abstract is below:

Functional Federated Learning in Erlang (ffl-erl) 
Gregor Ulm, Emil Gustavsson, and Mats Jirstrand

The functional programming language Erlang is well-suited
for concurrent and distributed applications, but numerical
computing is not seen as one of its strengths. Yet, the
recent introduction of Federated Learning, which leverages
client devices for decentralized machine learning tasks,
while a central server updates and distributes a global
model, motivated us to explore how well Erlang is suited to
that problem. We present the Federated Learning framework
ffl-erl and evaluate it in two scenarios: one in which the
entire system has been written in Erlang, and another in
which Erlang is relegated to coordinating client processes
that rely on performing numerical computations in the
programming language C. There is a concurrent as well as a
distributed implementation of each case. We show that Erlang
incurs a performance penalty, but for certain use cases this
may not be detrimental, considering the trade-off between
speed of development (Erlang) versus performance (C). Thus,
Erlang may be a viable alternative to C for some practical
machine learning tasks.