際際滷shows by User: aktarcar / http://www.slideshare.net/images/logo.gif 際際滷shows by User: aktarcar / Sat, 28 Mar 2020 14:19:59 GMT 際際滷Share feed for 際際滷shows by User: aktarcar Privacy preserving machine_learning_current_landscape /slideshow/privacy-preserving-machinelearningcurrentlandscape/231034615 privacypreservingmachinelearningcurrentlandscape-200328142000
Privacy preserving machine learning is an emerging field which is in active research. The most prolific successful machine learning models today are built by aggregating all data together at a central location. While centralised techniques are great , there are plenty of scenarios such as user privacy, legal concerns ,business competitiveness or bandwidth limitations ,wherein data cannot be aggregated together. Federated Learningcan help overcome all these challenges with its decentralised strategy for building machine learning models. Paired with privacy preserving techniques such as encryption and differential privacy, Federated Learning presents a promising new way for advancing machine learning solutions.]]>
Privacy preserving machine learning is an emerging field which is in active research. The most prolific successful machine learning models today are built by aggregating all data together at a central location. While centralised techniques are great , there are plenty of scenarios such as user privacy, legal concerns ,business competitiveness or bandwidth limitations ,wherein data cannot be aggregated together. Federated Learningcan help overcome all these challenges with its decentralised strategy for building machine learning models. Paired with privacy preserving techniques such as encryption and differential privacy, Federated Learning presents a promising new way for advancing machine learning solutions.]]> Sat, 28 Mar 2020 14:19:59 GMT /slideshow/privacy-preserving-machinelearningcurrentlandscape/231034615 aktarcar@slideshare.net(aktarcar) Privacy preserving machine_learning_current_landscape aktarcar Privacy preserving machine learning is an emerging field which is in active research. The most prolific successful machine learning models today are built by aggregating all data together at a central location. While centralised techniques are great , there are plenty of scenarios such as user privacy, legal concerns ,business competitiveness or bandwidth limitations ,wherein data cannot be aggregated together. Federated Learningcan help overcome all these challenges with its decentralised strategy for building machine learning models. Paired with privacy preserving techniques such as encryption and differential privacy, Federated Learning presents a promising new way for advancing machine learning solutions. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/privacypreservingmachinelearningcurrentlandscape-200328142000-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Privacy preserving machine learning is an emerging field which is in active research. The most prolific successful machine learning models today are built by aggregating all data together at a central location. While centralised techniques are great , there are plenty of scenarios such as user privacy, legal concerns ,business competitiveness or bandwidth limitations ,wherein data cannot be aggregated together. Federated Learningcan help overcome all these challenges with its decentralised strategy for building machine learning models. Paired with privacy preserving techniques such as encryption and differential privacy, Federated Learning presents a promising new way for advancing machine learning solutions.
Privacy preserving machine_learning_current_landscape from Amogh Kamat Tarcar
]]> 183 0 https://cdn.slidesharecdn.com/ss_thumbnails/privacypreservingmachinelearningcurrentlandscape-200328142000-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 https://cdn.slidesharecdn.com/profile-photo-aktarcar-48x48.jpg?cb=1596706721 Researching on unobtrusive authentication using Neural networks and Mobile inertial sensors.