10-719 Federated and Collaborative Learning
12 units, Carnegie Mellon University
Machine learning pipelines are increasingly powered by data from multiple sources and stakeholders. Techniques for collaborative learning, such as federated learning, stand to power a new generation of machine learning applications by enabling coordinated, trustworthy learning between multiple parties and across diverse data sources. To do so, novel approaches must be developed that improve the accuracy and efficiency of learning across siloed data mitigate risk and protect data privacy and ownership and incorporate social and economic principles that incentivize data sharing and provide trustworthy cooperative learning schemes. This seminar course will cover various aspects of federated and collaborative learning, with a focus on recent research developments and discussion on future directions. Students will participate in leading discussion sessions and will complete a course project in the general area of collaborative learning.
Prerequisites: 10-315 (min C) or 10-701 (min C) or 10-715 (min C) or 10-601 (min C)
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