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Privacy-Preserving Machine Learning on Graphs

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Distributed learning is the key for enabling training of modern large-scale machine learning models, through parallelising the learning process. Collaborative learning is essential for learning from privacy-sensitive data that is distributed across various ...

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The Privacy Power of Correlated Noise in Decentralized Learning

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Decentralized learning is appealing as it enables the scalable usage of large amounts of distributed data and resources (without resorting to any central entity), while promoting privacy since every user minimizes the direct exposure of their data. Yet, wi ...

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Graph Neural Networks (GNNs) have become a popular tool for learning on graphs, but their widespread use raises privacy concerns as graph data can contain personal or sensitive information. Differentially private GNN models have been recently proposed to p ...

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Few-shot Learning for Efficient and Effective Machine Learning Model Adaptation

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Random matrix methods for high-dimensional machine learning models

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