A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
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Lazydit introduces a lazy learning approach to accelerate Diffusion Transformers by selectively skipping redundant computations during inference.
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Proposes efficient privacy-preserving machine learning for blockchain networks, addressing scalability and confidentiality challenges in decentralized applications.
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A survey of privacy-preserving machine learning techniques for e-health applications, covering methods, discussion, and future research directions.
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MD-ML introduces a super fast privacy-preserving machine learning protocol with malicious security against a dishonest majority.
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This paper provides a taxonomy of privacy-preserving machine learning approaches for cloud computing and discusses challenges of applying them to IoT.
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Proposes an efficient privacy-preserving machine learning scheme for hierarchical distributed systems by modifying and improving the collaborative learning algorithm.
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The paper proposes a differential approach for data and classification service-based privacy-preserving machine learning in cloud environments, building on prior work in differentially private machine learning.
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A privacy-preserving FCNN framework using TFHE-based inference for secure medical image analysis on MedMNIST.
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HT2ML combines homomorphic encryption and trusted execution environments to enable efficient privacy-preserving machine learning on outsourced cloud data.
Rahul Rachuri, Ajith Suresh
Trident presents an efficient four-party framework for privacy-preserving machine learning, introducing specialized conversions for secure computation.
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Proposes PFMLP, a multi-party privacy-preserving machine learning framework combining partially homomorphic encryption with federated learning.
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Fortuna is an open-source library for uncertainty quantification in deep learning, addressing the gap between theory and practice.