SlideShare a Scribd company logo
Enabling Smart Manufacturing with Federated AI at
the Edge
Bin Cheng (bin.cheng@neclab.eu)
NEC Labs Europe, Heidelberg, Germany
Our Vision: Smart Manufacturing with Edge AI
1
cloud-edge
physical world, lots of moving or fixed edges with cameras and sensors
cloud
edge edge edge
Situation
aware APPs
edge
React on real-time
situations
Customers The knowledge required by applications can be
automatically derived and optimized at cloud and edges
towards the overall business objectives from customersBusiness
objectives
Situation
aware APPs
Situation
aware APPs
Global
knowledge
Regional
knowledge
Local
knowledge
Reacting on
real-time situations
Two cases for Edge AI: Distributed AI and Federated AI
2
cloud
e1 e2 e3
Domain A Domain B
cloud
e1 e2 e3
Distributed AI within
single trusted domain
Federated AI across multiple
untrusted domains
FogFlow for Distributed AI
3
Fog Function in FogFlow: Serverless Fog Computing
4
Data-centric fog function for dynamic composition of your AI pipelines
Adaptive AI Pipelines over Cloud and Edges
5
cloud
edg
e
edg
e
edge
cable
4G
wifi
wifi
# of possible configurations
• exponentially increases as the number of
tasks and their parameters
Deployment must be changed according to the
runtime environment and the current workload, in
order to fulfill the expected QoS
Mauricio Fadel Argerich, Bin Cheng, and Jonathan Fürst. "Reinforcement Learning based Orchestration for Elastic Services." WF-I
Reinforcement Learning based Orchestration
Federated AI over Multiple Domains
6
Domain A
Domain
experts
Domain B
Domain experts
Researcher
Domain C
samples
dataset A dataset B
system A system B
classifierclassifier raw data raw data
Function
Catalog
Publish
label functions
Publish/update
label functions
Publish/update
label functions
Select label functions
and also exchange
relevant models
Label
functions
Select label functions
and exchange
relevant models
Function Catalog: The Zoo of Data Processing Functions
7
data Sampling cleaning annotating labelling training predicting decisions
Data preparation (80% of the total effort)
Data Usage Control cross Domains
8
HyperLedge/Blockchain
(accountability)
data AI
functions
Usage
policy
Usage
evidence
data
data
AI
functions
AI
functions
Honest but curious
(semi-trusted model)
follows protocol but will try to learn
as much information as possible,
without actively “cheating”
Use Case: Defect Product Detection for Smart Factory
9
Workbench NWorkbench A
Marketplace
for AI Models
Defect Product
Detection
Local AI model creation
(labelling, training,
and learning at edges)
Sharing of AI models
for defect goods
Local AI model
federation and
usage
……
Thank you!
10

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Enabling Smart Manufacturing with Federated AI at the Edge

  • 1. Enabling Smart Manufacturing with Federated AI at the Edge Bin Cheng (bin.cheng@neclab.eu) NEC Labs Europe, Heidelberg, Germany
  • 2. Our Vision: Smart Manufacturing with Edge AI 1 cloud-edge physical world, lots of moving or fixed edges with cameras and sensors cloud edge edge edge Situation aware APPs edge React on real-time situations Customers The knowledge required by applications can be automatically derived and optimized at cloud and edges towards the overall business objectives from customersBusiness objectives Situation aware APPs Situation aware APPs Global knowledge Regional knowledge Local knowledge Reacting on real-time situations
  • 3. Two cases for Edge AI: Distributed AI and Federated AI 2 cloud e1 e2 e3 Domain A Domain B cloud e1 e2 e3 Distributed AI within single trusted domain Federated AI across multiple untrusted domains
  • 5. Fog Function in FogFlow: Serverless Fog Computing 4 Data-centric fog function for dynamic composition of your AI pipelines
  • 6. Adaptive AI Pipelines over Cloud and Edges 5 cloud edg e edg e edge cable 4G wifi wifi # of possible configurations • exponentially increases as the number of tasks and their parameters Deployment must be changed according to the runtime environment and the current workload, in order to fulfill the expected QoS Mauricio Fadel Argerich, Bin Cheng, and Jonathan Fürst. "Reinforcement Learning based Orchestration for Elastic Services." WF-I Reinforcement Learning based Orchestration
  • 7. Federated AI over Multiple Domains 6 Domain A Domain experts Domain B Domain experts Researcher Domain C samples dataset A dataset B system A system B classifierclassifier raw data raw data Function Catalog Publish label functions Publish/update label functions Publish/update label functions Select label functions and also exchange relevant models Label functions Select label functions and exchange relevant models
  • 8. Function Catalog: The Zoo of Data Processing Functions 7 data Sampling cleaning annotating labelling training predicting decisions Data preparation (80% of the total effort)
  • 9. Data Usage Control cross Domains 8 HyperLedge/Blockchain (accountability) data AI functions Usage policy Usage evidence data data AI functions AI functions Honest but curious (semi-trusted model) follows protocol but will try to learn as much information as possible, without actively “cheating”
  • 10. Use Case: Defect Product Detection for Smart Factory 9 Workbench NWorkbench A Marketplace for AI Models Defect Product Detection Local AI model creation (labelling, training, and learning at edges) Sharing of AI models for defect goods Local AI model federation and usage ……