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Developing a competitive computer vision foundation model in a privacy-preserving and responsible manner. This work aims to lead to AI that can be trusted across the whole lifecycle of AI development.
Related Publications
Federated learning (FL) promotes decentralized training while prioritizing data confidentiality. However, its application on resource-constrained devices is challenging due to the high demand for computation and memory resources for training deep learning models. Neural netw…
Federated learning has become a popular method to learn from decentralized heterogeneous data. Federated semi-supervised learning (FSSL) emerges to train models from a small fraction of labeled data due to label scarcity on decentralized clients. Existing FSSL methods assume…
Related Blog Posts
Advancements in Federating Learning Highlighted in Papers Presented at ICCV 2023
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Privacy-Preserving Machine Learning Blog Series: Practicing Privacy by Design
Privacy-Preserving Machine Learning Blog SeriesAt Sony AI, the Privacy-Preserving Machine Learning (PPML) team focuses on fundamental and applied research in computer vision privac…
Recent Breakthroughs Tackle Challenges in Federated Learning
Privacy-Preserving Machine Learning Blog SeriesAt Sony AI, the Privacy-Preserving Machine Learning (PPML) team focuses on fundamental and applied research in computer vision privac…
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