byteLAKE's Federated Learning: bringing collaborative learning with privacy to AI. Enabling machines and AI models to learn from each other across global networks. More at: www.bytelake.com/en/federated-learning

1. byteLAKE
We build intelligent software and hardware solutions
Federated Learning
in action
(example demo scenario for manufacturing)

2. 2
Machine
Learning
Sensor
Data
Time
Local training & inferencing
Lenovo’s Tiny device
Aggregating local models
& redistributing updates
Federated Learning
Lenovo’s Data Center
How it works

3. ➢ learning to predict pressure changes (locally)
➢ learning locally & from already trained systems (federated learning)
Simulation
Fan 1 Fan 2
Barometric
pressure
sensor
Filter 1 Filter 2
Styrofoam
balls
Flying Styrofoam balls…
… simulate filters clogging
& pressure changes.
Demo

4. 4
Demo

5. Lenovo’s
Tiny Think Centre
Simulation
- Arduino Uno Rev3
- Barometric Pressure Sensor (BMP180)
- Ultrasonic Distance Sensor (HC-SR04)
- Federated learning
- Machine learning model
(based on regression method)
Lenovo’s Data Center
Technology highlights
Software highlights
- Arduino Software (IDE)
- PC/Server: C++
- GUI: QT v5
- Graphics: OpenGL
- Client-Server: Unix Sockets
- Server: OpenMP
Demo

6. ➢System reached the accuracy of 95-98%
(federated learning)
➢At the same time,
local model reached 40%
(tens of iterations to reach
60-80% accuracy)
6
Simulation results
Demo

7. 7
Read more:
Video:
www.byteLAKE.com/en/AISummit2018
www.byteLAKE.com/en/AISummit2018-video
Demo

8. Federated Learning
enables
devices (IoT) to learn
from each other
AI Summit, San Francisco, CA, booth #607 (Sept. 19-20 2018)
Meet & to see a live demo

9. ✓Enable Scalability
(Decentralized AI enables IoT/devices to learn from each other)
✓Solve low-throughput and high-latency challenges
(Local AI models reduce latencies, lower power consumption)
✓Improve accuracy
(Have smarter models via aggregation of many local models)
✓Reduce training time
(Benefit from local training and already trained models in the neighborhood)
✓Lower the cost of training
(Bringing data from all devices is expensive)
✓Ensure privacy
(Sensitive data stays local)
9
Benefits of
Federated Learning

10. Explore new possibilities
with federated learning
10
Healthcare
Aggregate knowledge
from many specialists
Industry
Enable devices to learn
from each other
Expert Systems
Better decisions

11. Embrace the new possibilities of
Augmented Intelligence
with

12. We build intelligent software and
hardware solutions.
We port and optimize algorithms for
parallel, CPU+GPU HPC
architectures.
We deploy AI on data centers,
in the cloud and on constrained,
embedded devices (AI on Edge).
byteLAKE We are specialists in:
www.byteLAKE.com
Machine Learning
Deep Learning
Computer Vision
High Performance Computing
Heterogeneous Computing
Edge Intelligence
Enabling companies to become more efficient
and increase competitiveness in their markets.
We are a team of scientists, programmers, designers
and technology enthusiasts helping industries incorporate
AI techniques into products.