=Paper=
{{Paper
|id=Vol-2388/short2
|storemode=property
|title=(De)centralized AI Solutions
|pdfUrl=https://ceur-ws.org/Vol-2388/short2.pdf
|volume=Vol-2388
|authors=Patrick Schmid,Matthias Lederer
}}
==(De)centralized AI Solutions==
https://ceur-ws.org/Vol-2388/short2.pdf
(De)centralized AI Solutions
Technological and commercial design parameters
Patrick Schmid Matthias Lederer
ISM Munich, Germany ISM Munich, Germany
Patrick.Schmid@ism.de Matthias.Lederer@ism.de
ABSTRACT concept that has been introduced in 2017 (the
Developments concerning artificial intelligence corresponding algorithm already in 2016) is to
and machine learning have gained a lot of traction parallelize computing for machine learning in
recently. Originally thought of as originating from order to decrease the latency [4].
a centralized device like a mainframe computer, a This development can be put into the context of
major modification has been introduced in 2017 decentralized autonomous organizations [5] since
with the “federated learning” concept performed it is comparable to the fundamental idea of cross-
by decentralized agents. As this constitutes a functional working in a BPM (Business Process
relatively new development, this research-in- Management) context. The often mentioned prime
progress contribution addresses the topic of example is the parallelized machine learning for
centralized vs. decentralized artificial intelligence image recognition on smart phone agents [4]. This
in a broader context and in more detail. After a short paper discusses major topics of centralized
brief introduction of the new concept with a vs. decentralized AI in more detail and displays
mentioning of accompanying developments, a further application areas.
selection of most relevant technological aspects as There are several accompanying developments
well as commercial design parameters to decide on that encourage decentralization: for example
(in particular the smart factory setting) is outlined. “softwarization” and “disappearing internet of
things” [2]. The idea is roughly that devices are
KEYWORDS more becoming like terminals as end-nodes of a
Artificial Intelligence, Decentralized Autonomous Organization, network, thus spanning a seamless continuum
Disappearing Internet of Things, Embedded AI Solutions,
Federated Learning, Machine Learning, Smart Factory, from the devices to the cloud [2]. Here in
Softwarization. particular a lot of computation already takes place
at the devices, so that computation is moving
1 Introduction and Background “toward the edge”, hence enforcing Edge and Fog
Computing (here Fog Computing covers the gap
As artificial intelligence (AI) applications
between Cloud and Edge Computing) [2]. As
typically involve machine learning (ML) routines
simple computations can typically evolve into
relying on the processing of big data, the time
machine learning routines, there seems to be a
needed for computational tasks is seen as a major
similar development for decentralized AI
constraint [1]. This applies even more if the
solutions in a broader context (AI at the edge
learning procedures are performed on a central
instead of simple computations at the edge) and
device like a mainframe computer [2], [3]. One of
not only in the aforementioned image recognition
the main ideas behind the “federated learning”
example. So the decentralized devices can not only
S-BPM ONE 2019, June 26-28, Sevilla, Spain Patrick Schmid, Matthias Lederer
be seen as contributors to a consolidated learning considerations, federated learning can
procedure, but in fact learning for their own constitute one method to avoid bottlenecks and
purpose as well as exchanging information and/or speed up critical process steps.
synchronizing when it seems useful. In short, AI is 2. Quality: Apart from “just” parallelizing tasks
no longer purely centralized and also no longer for ML procedures, where the volume of the
data that has to be processed is very large, the
purely a result of one single participant in learning.
learning of several agents can be used to
So, there are two distinct conceptual setups: either improve the overall quality of the respective
several agents contribute to a consolidated ML routine [6]. Typically, this is done via
learning in a hierarchical structure without ensemble methods of machine learning (for
substantially learning for themselves (for example instance via “bagging” or “boosting” to name
when they only perform a limited number of a few examples) [7]. One might question
iterations [6]) or several agents synchronize on the whether quality is strictly improved by
same hierarchy level meaning that they produce an consolidating the inputs of various learning
aggregated model that is shared between all of agents (as they might provide misleading
them. adjustments). The quality improvement cannot
be strictly guaranteed and is dependent on the
method used for aggregation, but typically,
2 Relevant technological aspects more input from agents should generate the
There are several relevant (technological) aspects potential for superior quality in learnings [7].
with accompanying challenges that have to be Bagging and boosting may be the most widely
known procedures for this, but of course there
understood and mastered in order to decide on the
exist many different variants with their own
parameters of central/decentral AI approaches in a nuances / strengths and this topic is very much
meaningful manner: an area of active research [7]. Detailed analysis
is needed what specific algorithmic procedure
1. Speed: As already mentioned, multiple agents in what setup best leverages the already gained
can be regarded as a kind of parallelized insights of the single distributed learners (to
computing mechanism in order to use illustrate the nuances of such a procedure, it
available time windows more efficiently. This shall be mentioned that the newly developed
objective is in particular achieved via federated learning concept also considers
delegating subtasks in a hierarchical setup to which subparts of the training set are sent to
various agents hence improving “virtually” the the distributed clients) [4].
computing power. Furthermore, as the 3. Compatibility: The agents that participate in
computational procedures are performed at the hierarchical decentralized learning or same
local clients at the edge (in the sense of Edge level synchronization must adhere to the same
Computing), several data transmissions and machine learning method / model as a
information exchanges with the cloud become necessary condition in order to facilitate
obsolete [6]. In summary, speed is not only consolidation and exchange of information
increased by higher computational power due (for example weight functions). Of course, as
to the number of participating devices, but also there exists a huge variety of machine learning
by reduction of now unnecessary back-forth procedures which are all performed
transmissions of the training data which is differently, these methods need to be aligned.
stored locally [6]. Additionally, a reduction of The same applies to questions of
(where applicable) communication costs can implementation, since, for example, in a smart
occur [6]. So under business process factory various proprietary devices with
(De)centralized AI solutions S-BPM ONE 2019, June 26-28, Sevilla, Spain
underlying architectures and software [9] in science and business practice: the
solutions have to work together [8]. “embedded AI solutions” and applications in the
4. Security/Privacy: The security challenges smart factory setting.
arise when a lot of not fully trustworthy agents
contribute to the decentralized learning (either 3.1 Embedded AI Solutions
hierarchical or same level) [6]. One can refer There is one related thinking approach by C. E.
here to discussions about security in service- Bouee [10] that deserves special attention as he
oriented process environments - there are was the first to address the decentralization issue
similar challenges (for example, asking for a
of AI in full force (in particular with respect to its
trustful registry). It could be that information
commercial dimension). He envisions an enforced
supplied for consolidation is "manipulated"
intentionally to distort the quality of learning development and usage of “portable AI” solutions
[6]. Here lies the corresponding challenge of which are decentralized AI solutions that can be
suppressing such occurrences. Apart from that integrated (embedded) into all kinds of devices,
challenge, decentralized learning is commonly typically resembling a highly individualized smart
regarded as a way to increase privacy since assistant [10]. Furthermore, he calls into doubt that
confidential data can be kept locally and only the large digital platforms that dominate the tech
processed (anonymized) information, like sector today will dominate the market for portable
weightings of ML routines, is then shared [6]. AI solutions in the future and even believes that
portable AI could end their monopoly [10].
Considering these technological aspects, it can be
discerned that there are several potential
He provides several examples for such emerging
advantages which can be gained from the
companies that are active in the segment of
application of decentralized learning scenarios.
embedded AI solutions, thereby illustrating the
However, also respective challenges need to be
parameter decision of (de)centralization [10]:
addressed in order to fully realize these potential
advantages.
1. Snips (from France) attempts to put “AI in
every device”, hence creating “a unified voice
strategy with a complete range of interface
offerings - from simple voice commands to
3 Commercial application areas and comprehensive natural language voice
use cases recognition” [11]. So they are exactly
addressing the softwarization topic already
After understanding technological aspects, described.
business questions and typical use cases that build
on the decentralized learning idea shall be 2. Arago (from Germany) focuses on the B2B
addressed. Generally, decentralized artificial sector and its solutions are meant “to automate
intelligence can be applied to any field where enterprise IT and business operations” via
currently artificial intelligence solutions are machines “with human problem-solving
deployed since there are typically multiple devices skills” that underwent supervised training
in use which are up to now not connected regularly [12]. Hence, they essentially support the
in a hierarchical or same level relationship. In the automation of business processes and show the
potential of decentralized AI solutions therein.
following, two selected areas are outlined since
they play a major role in the ongoing discussion
S-BPM ONE 2019, June 26-28, Sevilla, Spain Patrick Schmid, Matthias Lederer
3. SenseTime (from China) is active in many Highly individualized smart assistants are then AI
deep learning and supercomputing areas, in solutions in their most decentralized form as these
particular with its independently developed solutions do not just complement technical
large-scale training system it also employs devices, but also every single end user [10]. Bouee
decentralized machine learning [13]. elaborates that basically new business models will
Therefore, they act as a kind of orchestrator for
arise from this as these individualized smart
decentralized learning.
assistants can act as much more strict gatekeepers
From this exemplary listing one can already get an for personal decisions than common smart
idea in what fields entrant companies try to capture assistants currently do [10]. An interesting
market share from the incumbent large tech immediately arising question is, whether
companies. Foremost, they are supposed to have a companies can and will deliberately set up barriers
competitive advantage over the incumbent such that personalized smart assistants cannot be
companies since they are not burdened with used to full extent (much like the already existing
having negative publicity concerning data privacy bot-barriers for travel websites).
and security issues. Furthermore, it is conceivable
that they gain advantages from more appealing The topic of decentralization is of particular
product offerings with higher individualization relevance in a likely “second wave” of
and “tailor-made” fit [10]. One further argument digitization. While in the first (B2C marketing-
of Bouee for the competitive edge of these oriented) wave large (U.S.) companies use
emerging firms is that data becomes obsolete centrally stored personalized data, manufacturing
faster and that therefore the advantages which processes and B2B transactions will then be
incumbent companies possess from their already digitized in a second wave. Here, the tendencies to
amassed data consequently diminishes very centralize AI will likely not take place due to
quickly [10]. However, on the contrary it can be already established physical equipment (with
argued that the large incumbent companies with various therein embedded software standards)
their already existing wide outreach and which is difficult to merge. Furthermore, the
computational power do still retain an advantage possible fear of process participants of losing
in this respect since bigger amounts of data are control over their data encourages
created faster and hence require much more decentralization.
computational power for assessing and evaluating 3.2 Smart Factory
these large volumes. This is certainly the case with
The other large application field that should be
respect to potent cloud offerings which heavily
discussed in more detail is the smart factory setting
rely on the magnitude of computational strength.
which was originally envisioned by the German
Ministry of Education and Research (BMBF) [15].
The aspect of highly individualized smart
“Smart factory” refers to manufacturing processes
assistants is also very interesting, as this is
(including resources) in which relevant involved
somehow the opposite of a smart assistant
machines and devices are all equipped with
provided by a large technology company that
sensors and perform typical smart tasks, like
could be sometimes prone “to lose its voice” (as
Condition Monitoring, Diagnosis for Maintenance
featured in a famous commercial) [14].
and Optimization of Processes, that can be
improved via machine learning [16]. It is certainly
(De)centralized AI solutions S-BPM ONE 2019, June 26-28, Sevilla, Spain
conceivable that an enterprise has multiple smart concern certain types of machines, whereas
factories in use that either share the full Optimization of Processes might depend on the
infrastructure or at least key components. This is level in a multi-level production process.
then the “factory network” scenario that strives to However, if a sufficient similarity can be
optimize network performance [17]. This typically guaranteed, such (de)centralized learning concepts
addresses the four basic layers of the “Life Cycle make a lot of sense. One could argue that different
Value Stream” of the RAMI 4.0 framework: tasks are never similar enough, however,
Asset, Integration, Communication and considering that a factory is a highly standardized
Information [18]. setting, it is conceivable that there might be a
As data basically is the “lifeblood” of smart smart transformation/translation of corresponding
factories, an evident approach for maximizing use cases.
network performance would be to harness the
available data of the whole network in a more But not only the question “whether” it does make
efficient way by centralizing the decentralized sense, also the question “how often or regular”
learnings of specific similar tasks. such a synchronization shall take place has to be
answered. In a mimimum scenario, the
Here one has to carefully distinguish according to synchronization can take place in typical inactive
the degree of similarity. To give two extreme periods like production pauses or overnight as not
examples: Concerning autonomous driving it to interfere with the regular business operations
makes a lot of sense to align and share learnings, (thereby reducing process speed). This is in
since increasing the security of autonomous particular apt for devices and components used in
driving has highest priority and conditions of process steps that require fast decisions and
streets are highly standardized. However, if actions. However, if devices and components are
natural language processing is concerned, it seems operating on a much slower time scale (for
recommendable not to mix and interchange example registering inbound commodity flows or
training data or learning weights of different outbound logistics of finished products) the
dialects within a language (although they do synchronization could take place continuously.
represent the same language) as this rather tends Similar questions are relevant when integrating
to create confusion instead of resolving it. partners in SCM (Supply Chain Management) or
In the same manner one has to proceed in the smart PLM (Product Lifecycle Management) processes.
factory setting, since there might be tasks and However, in modern value networks which rely on
routines that are more similar to each other than to data exchange for smart/reliable decisions, well-
the rest (depending on the type of smart factory). known process standards (e.g., EDI, flat XML)
Certainly, such training and synchronization may not be sufficient.
procedures can be applied to the same type of task
within the same type of factory. But generalization According to experts of “Internet of Business” the
to similar tasks is, as always, accompanied by 10 smart factory trends to watch in 2019 are the
certain challenges in deciding whether the following [19]:
similarity is sufficient enough. Here it is
conceivable that differences for Condition
Monitoring and Diagnosis for Maintenance
S-BPM ONE 2019, June 26-28, Sevilla, Spain Patrick Schmid, Matthias Lederer
1. “Collaborative robots will augment agendas. The strength of such shared learnings
workforces” should in particular become apparent in cases
2. “Cloud robotics & APIs will give where there is a lack of suitable training material,
manufacturers greater control” for example when whole processes in smart
3. “Robotics-as-a-service will make robotics factory are redesigned according to machine
viable for smaller manufacturers”
learning insights. Although this would be a
4. “5G & Multi-access Edge Computing (MEC)
will help keep factory workers informed” desirable feature of the smart factory, this redesign
5. “Edge computing will see new use cases” of processes is certainly regarded as a much more
6. “Cybersecurity will be given greater priority” complex task than just checking the need for
7. “AI & advanced analytics will become near- maintenance of a machine since much more
ubiquitous” factors (like implications for following processes
8. “Digital twins will be employed more widely and business partners) have to be considered and
across manufacturing and the supply chain” evaluated.
9. “Additive manufacturing will be used to create
final products”
10. “Wearables will become commonplace on the 4 Conclusion
factory floor” In this short paper relevant design aspects and
implications of connecting formerly unconnected
In this list there are two trends that have a AI solutions with each other were discussed as
particular high relevance for our discussed topic, well as the topic of creating much more agents that
namely “Edge computing will see new use cases” are amenable to such setups. The connection of AI
and “AI & advanced analytics will become near- solutions can either be understood as delegating
ubiquitous”. By design, the components and tasks in a hierarchical setup or as a kind of
devices of a smart factory are meant to make synchronization between agents on an equal
autonomous decisions. So in time-critical footing. The potential of such setups that
processes it is wanted that more computation is exemplify a much greater extent of
moving towards the edge in order to increase the interconnection has yet to be realized, but it is
speed and decrease the latency. Also crucial is the expected to be adopted and enforced much more
rising importance and spread of AI & advanced in upcoming developments, for example on the
analytics, since this first ensures that local AI way to the fully integrated smart factory.
solutions are present and, furthermore, that there
exists potential which can be realized via their
alignment. It is certainly highly dependent on the
particular use case whether the cooperation of the
distributed AI solutions takes place on the same
level or in a “master-slave” setup.
But it has to be noted that the aforementioned
centralization procedures for AI solutions are not
yet to be anticipated in full force for the current
year, hence pointing out potentials for realizations
that still have to find their place on the respective
(De)centralized AI solutions S-BPM ONE 2019, June 26-28, Sevilla, Spain
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