The document discusses privacy-aware analytics at the edge using federated learning, highlighting challenges due to data growth from IoT devices and the necessity for secure data handling amid increasing privacy regulations. It outlines federated learning as a solution that promotes local data processing while minimizing data transfer, ultimately enhancing personalization and private data retention. Additionally, it touches on the limitations of centralized analytics and the advantages of federated systems in terms of scalability and reduced infrastructure costs.

1. Privacy aware analytics at
edge using Federated
Learning
Arindam Banerjee
Data Scientist, Ericsson
1Arindam Banerjee

2. Shaping the next decade…
• 5G
• Exponential growth of
Smart devices
• Exponential growth of
data
• Stricter data privacy
and protection laws
• Automation and
Analytics
2Arindam Banerjee

3. 5G and IoT: Ushering in
a new era
• Foundation for realizing the full
potential of IoT.
• 550 million 5G subscriptions in
2022 - Ericsson AB’s Mobility
Report.
• A fertile ground for innovations
and customers engagements.
• Network slicing. Segmenting
network:
enhanced Mobile Broadband
(eMBB),
Ultra Reliable Low Latency
Communications (URLLC),
massive Machine Type
3Arindam Banerjee

4. Exponential growth
of Data
• Billions of IoT sensors
and mobile devices at
edge.
• Uncertainty about network
quality.
• Intermittently available.
• From massive data to
context-aware, smarter
apps.
4Arindam Banerjee

5. Privacy and Security
• IoT data is growing fast but
security
remains a big hurdle.
• Hyper-personalization comes with
loss of privacy.
• Laws and regulations:
Health Insurance Portability
and Accountability Act
(HIPAA),
General Data Protection 5Arindam Banerjee

6. Problems faced in CentralizedAnalytics
Edge to cloud
data
transmission
Bottleneck -
Network
bandwidth
Security and
privacy
Infrastructure
cost
Underutilized
resources at
edge
6Arindam Banerjee

7. Federated Learning (FL)
• Collaborative learning
• Downloaded model learns from local data
• Compressed update is sent back to the cloud
• Federated averaging
• New model is communicated back to edge
• Improved local inference empowers
personalization.
7Arindam Banerjee

8. Ref.: Ericsson Technology Review, 09-2019. 8Arindam Banerjee

9. Features of Federated Learning
Edge IoT
devices have
limited
network
bandwidth.
Devices are
intermittently
available for
training.
Device may
choose not to
participate in
the training.
Massive number
of devices but
inconsistent.
Naturally
arising non-
IID partition.
Massively
parallel.
9Arindam Banerjee

10. Advantages of Federated
Learning
• Privacy:
Data localization
Data retention
• Less data transfer - less Network
Bandwidth
• Better battery life.
• Scalability.
• Less infrastructure cost.
• Low latency for inference.
10Arindam Banerjee

11. • High convergence time.
• Unavailability of edge
devices.
• Irregular or missed
updates.
11Arindam Banerjee

12. Model Poisoning
Image Source: Clement Fung et al. – “Mitigating Sybils in Federated
Learning Poisoning”
12Arindam Banerjee

13. How about
existing
distributed
computing
frameworks?
13Arindam Banerjee

14. SET OF
COORDINATING
ENTITIES
SYSTEM-
CONTROLLED
HIGH
AVAILABILITY
RELIABILITY
ADDRESSABLE
AND FIXED
NODES
14Arindam Banerjee

15. Tools
OpenMined
PySyft
Google
TensorFlow
Federated
15Arindam Banerjee

16. Questions
16Arindam Banerjee