Type: Bachelor Master UROP
Human mobility can be modeled using smartphone data, however, such data represents sensitive information, and the collection of those threatens the privacy of the users involved. The recent introduction of federated learning, a privacy-preserving approach to build machine and deep learning models, represents a promising technique to solve the privacy issue.
In the context of a BSc project, you will: overview methods based on federated-learning  with the aim to use them for human mobility modeling; (ii) use an existing dataset to train a deep learning model for human mobility modeling (e.g., next task prediction)  using federated learning; (iii) analyze the behavior of the model for a variety of parameters (e.g., dataset size, model size, etc.); (iv) summarize the results and propose future work.
In the context of an MSc thesis or UROP project, you will: (i) create an overview of privacy-aware human mobility modeling approaches; (ii) pre-preprocess/normalize datasets to a common format (e.g., ); ; (iii) create advanced privacy-aware models for human mobility modeling (e.g., Transformer networks  using federated learning ); (iv) compare the results to existing baselines (e.g., baselines based on RNNs ,); (v) summarize the results and propose future work.
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- scikit-mobility: mobility analysis in Python
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- “Open Sourcing BERT: State-of-the-Art Pre-training for Natural Language Processing”. Google AI Blog. Retrieved 2019-11-27.
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