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Split Learning versus Federated Learning for Data Transparent ML, Camera Culture Group, MIT Media Lab

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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.

Publicado en: Tecnología

Split Learning versus Federated Learning for Data Transparent ML, Camera Culture Group, MIT Media Lab

  1. 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. 2. Pooling ‘small’ data No Exchange of Raw Patient Data
  3. 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. 4. ‘Invisible’ Health Data ‘Small Data’ ‘Small Data’‘Small Data’
  5. 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. 6. Gupta, Raskar ‘Distributed training of deep neural network over several agents’, 2017 No Exchange of Raw Patient Data Train Neural Nets
  7. 7. Training Deep Networks No sharing of Raw data Server Client Invisible Data / Data Friction
  8. 8. Ease Incentive Trust Regulation Ayushman Blockchain AI/ SplitNN Overcoming Data Friction
  9. 9. Automating ML : AI building AI Published in International Conference on Learning Representations (2017) Otkrist Gupta, Baker, Naik, Raskar, ICLR 2017 Teacher Student
  10. 10. Anonymize Protect Data Obfuscate Encrypt
  11. 11. Protect Raw Data Share Wisdom Data Utility Train Model EncryptSmashObfuscate Add Noise Private Data Protect Infer Statistics Anonymize Share Wisdom, Not Data
  12. 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. 13. Federated Learning Server Client1 Client2 Client3 ..
  14. 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. 15. Memory Compute Bandwidth ConvergenceFederated Split
  16. 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  

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