Project FLUTE
Breaking barriers for Federated Learning research at scale
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Recent research has shown that deploying ML models can, in some cases, implicate privacy in unexpected ways. For example, pretrained public language models that are fine-tuned on private data can be misused to recover private information, and very large language models…
Privacy in AI group explores questions related to user privacy and confidentiality in machine learning.
Scientists and engineers in the LEAP (Language, Learning, Audio, Privacy) research area make and harness advances in machine learning, natural language processing, and signal processing to: We advance the state-of-the-art in AI while providing practical solutions to real-world problems and…