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Researcher profile

Farinaz Koushanfar

7 papers in the PaperMetrix corpus

Publications

Papers by this author

  1. Adversarial Reprogramming of Sequence Classification Neural Networks.

    2018 · arXiv (Cornell University)

    Adversarial Reprogramming has demonstrated success in utilizing pre-trained neural network classifiers for alternative classification tasks without modification to the original network. An adversary in such an attack scenario trains an additive contribution to the inputs …

  2. A Taxonomy of Attacks on Federated Learning

    2020 · IEEE Security & Privacy

    Federated learning is a privacy-by-design framework that enables training deep neural networks from decentralized sources of data, but it is fraught with innumerable attack surfaces. We provide a taxonomy of recent attacks on federated learning …

  3. HASHTAG: Hash Signatures for Online Detection of Fault-Injection Attacks on Deep Neural Networks

    2021 · arXiv (Cornell University)

    We propose HASHTAG, the first framework that enables high-accuracy detection of fault-injection attacks on Deep Neural Networks (DNNs) with provable bounds on detection performance. Recent literature in fault-injection attacks shows the severe DNN accuracy degradation …

  4. AdaGL: Adaptive Learning for Agile Distributed Training of Gigantic GNNs

    2023

    Distributed GNN training on contemporary massive and densely connected graphs requires information aggregation from all neighboring nodes, which leads to an explosion of inter-server communications. This paper proposes AdaGL, a highly scalable end-to-end framework for …

  5. Magmaw: Modality-Agnostic Adversarial Attacks on Machine Learning-Based Wireless Communication Systems

    2023 · arXiv (Cornell University)

    Machine Learning (ML) has been instrumental in enabling joint transceiver optimization by merging all physical layer blocks of the end-to-end wireless communication systems. Although there have been a number of adversarial attacks on ML-based wireless …

  6. LayerCollapse: Adaptive compression of neural networks

    2023 · arXiv (Cornell University)

    Handling the ever-increasing scale of contemporary deep learning and transformer-based models poses a significant challenge. Overparameterized Transformer networks outperform prior art in Natural Language processing and Computer Vision. These models contain hundreds of millions of …

  7. ZORRO: Zero-Knowledge Robustness and Privacy for Split Learning (Full Version)

    2025 · arXiv (Cornell University)

    Split Learning (SL) is a distributed learning approach that enables resource-constrained clients to collaboratively train deep neural networks (DNNs) by offloading most layers to a central server while keeping in- and output layers on the …