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Bayesian Differential Privacy for Machine Learning

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Bayesian Differential Privacy for Machine Learning

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Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

Learning Robust and Adaptive Representations: from Interactions, for Interactions

Personalized Privacy-Preserving Distributed Learning on Heterogeneous Data

On the Privacy-Robustness-Utility Trilemma in Distributed Learning

Challenging the Assumptions: Rethinking Privacy, Bias, and Security in Machine Learning

Arbitrary Decisions are a Hidden Cost of Differentially Private Training

Privacy-Preserving Machine Learning on Graphs

Aiming beyond slight increases in accuracy

Federated Learning under Covariate Shifts with Generalization Guarantees

On the (In)security of Peer-to-Peer Decentralized Machine Learning