The Conference for Machine Learning Innovation

Privacy-preserving Machine Learning

Session
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Register until September 10:
✓ Raspberry Pi or C64 Mini for free
✓Save up to 313 €
✓10 % Team Discount
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✓ Raspberry Pi or C64 Mini for free
✓ Save up to $310
✓ 10% team discount
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Join the ML Revolution!
Register until July 2:
✓ Raspberry Pi or C64 Mini for free
✓ Save up to $310
✓ 10% team discount
Register Now
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Register until November 7th:
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✓10% Team Discount
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Join the ML Revolution!
Register until November 7th:
✓Save up to € 210
✓10% Team Discount
Register Now
Infos
Tuesday, December 10 2019
11:15 - 12:00
Room:
Saal A+B

Privacy-preserving machine learning is a subfield of machine learning in which the training of the model happens in such a way that the privacy of the data is preserved. Various approaches already exist but are not well established. At the same time, privacy considerations become more important. Among the approaches is federated learning for a decentralized training, whereby the data can stay at the place of origin and only learning updates or gradient updates are exchanged. Another approach is differential privacy – stochastic gradient descent whereby the learning algorithm of the neural network is modified so that single training examples do not affect the model too much. Thus, limited inference can be made from the model to the data it was trained on. In this talk we will understand both approaches and have a look on how to implement them with the help of TensorFlow.

This Session belongs to the Diese Session gehört zum Programm vom BerlinBerlin program. Take me to the program of . Hier geht es zum Programm von Online Edition Online Edition .

This Session belongs to the Diese Session gehört zum Programm vom BerlinBerlin program. Take me to the program of . Hier geht es zum Programm von Munich Munich .

This Session belongs to the Diese Session gehört zum Programm vom BerlinBerlin program. Take me to the program of . Hier geht es zum Programm von Singapore Singapore .

Take me to the full program of Zum vollständigen Programm von Berlin Berlin .

This Session Diese Session belongs to the gehört zum Programm von BerlinBerlin program. Take me to the current program of . Hier geht es zum aktuellen Programm von Online Edition Online Edition , Munich Munich , Singapore Singapore or oder Berlin Berlin .

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