Project page: https://splitlearning.github.io/ Papers: https://arxiv.org/search/cs?searchtype=author&query=Raskar Video: https://www.youtube.com/watch?v=8GtJ1bWHZvg Split learning for health: Distributed deep learning without sharing raw patient data: https://arxiv.org/pdf/1812.00564.pdf Distributed learning of deep neural network over multiple agents https://www.sciencedirect.com/science/article/pii/S1084804518301590 Otkrist Gupta, Ramesh Raskar, In domains such as health care and finance, shortage of labeled data and computational resources is a critical issue while developing machine learning algorithms. To address the issue of labeled data scarcity in training and deployment of neural network-based systems, we propose a new technique to train deep neural networks over several data sources. Our method allows for deep neural networks to be trained using data from multiple entities in a distributed fashion. We evaluate our algorithm on existing datasets and show that it obtains performance which is similar to a regular neural network trained on a single machine. We further extend it to incorporate semi-supervised learning when training with few labeled samples, and analyze any security concerns that may arise. Our algorithm paves the way for distributed training of deep neural networks in data sensitive applications when raw data may not be shared directly.

1. Raskar, Camera Culture, MIT Media Lab
Camera Culture
Ramesh Raskar
Data Transparent ML + Health
Privacy vs Societal Benefits
Training NN without Raw Data
Ramesh Raskar
Associate Professor
MIT

2. Pooling
‘small’ data
No Exchange of
Raw Patient Data

3. Technologies
Individual Organization Population
Physical
Diagnostic
Devices
EHR
DigitalGlobal
Pop-health
Health
Vault
Health
Data Market
Health
OS
Removing
Data Friction
New Capture
+ Analysis
Patient Scale  World Scale
Existing Data +
Techniques
 

4. ‘Invisible’ Health Data
‘Small Data’
‘Small Data’‘Small Data’

5. a. Distributed Data
b. Patient privacy
c. Incentives
d. ML Expertise
e. Labeling
Low
Bandwidth
Low
Compute
‘Small’ Data
ML for Health Data

6. Gupta, Raskar ‘Distributed training of deep neural network over several agents’, 2017
No Exchange
of Raw
Patient Data
Train
Neural Nets

7. Training Deep
Networks
No sharing of
Raw data
Server Client
Invisible Data / Data Friction

8. Ease Incentive Trust Regulation
Ayushman
Blockchain
AI/ SplitNN
Overcoming Data Friction

9. Automating ML : AI building AI
Published in International Conference on Learning Representations (2017)
Otkrist Gupta, Baker, Naik, Raskar, ICLR 2017
Teacher Student

10. Anonymize
Protect Data
Obfuscate Encrypt

11. Protect Raw
Data
Share
Wisdom
Data
Utility
Train Model
EncryptSmashObfuscate
Add Noise Private
Data
Protect
Infer
Statistics
Anonymize
Share Wisdom, Not Data

12. Federated Learning
Nets trained at Clients
Merged at Server
Differential Privacy
Obfuscate with noise
Hide unique samples
Homomorphic Encryption
Basic Math over Encrypted
Data (+, x)
Split Learning (MIT)
Nets split over network
Trained at both

13. Federated
Learning
Server
Client1 Client2 Client3 ..

14. Partial
Leakage
Differential
Privacy
Homomorphic
Encryption
Oblivious Transfer,
Garbled Circuits
Federated Learning
Split Learning
Protect
data
Distributed
Training
Inference but no training
Praneeth Vepakomma, Tristan Swedish, Otkrist Gupta, Abhi Dubey, Raskar 2018

15. Memory
Compute
Bandwidth
ConvergenceFederated
Split

16. Technologies
Individual Organization Population
Physical
Diagnostic
Devices
EHR
DigitalGlobal
Pop-health
Health
Vault
Health
Data Market
Health
OS
Removing
Data Friction
New Capture
+ Analysis
Patient Scale  World Scale
Existing Data +
Techniques
 