Rachid Guerraoui
5 papers in the PaperMetrix corpus
Papers by this author
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Byzantine-Tolerant Machine Learning
2017 · arXiv (Cornell University)
The growth of data, the need for scalability and the complexity of models used in modern machine learning calls for distributed implementations. Yet, as of today, distributed machine learning frameworks have largely ignored the possibility …
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Differential Privacy and Byzantine Resilience in SGD: Do They Add Up?
2021 · arXiv (Cornell University)
This paper addresses the problem of combining Byzantine resilience with privacy in machine learning (ML). Specifically, we study if a distributed implementation of the renowned Stochastic Gradient Descent (SGD) learning algorithm is feasible with both …
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Combining Differential Privacy and Byzantine Resilience in Distributed\n SGD
2021 · arXiv (Cornell University)
Privacy and Byzantine resilience (BR) are two crucial requirements of\nmodern-day distributed machine learning. The two concepts have been extensively\nstudied individually but the question of how to combine them effectively\nremains unanswered. This paper contributes to addressing …
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Oracular Byzantine Reliable Broadcast [Extended Version]
2022 · arXiv (Cornell University)
Byzantine Reliable Broadcast (BRB) is a fundamental distributed computing primitive, with applications ranging from notifications to asynchronous payment systems. Motivated by practical consideration, we study Client-Server Byzantine Reliable Broadcast (CSB), a multi-shot variant of BRB …
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Balancing Privacy, Robustness, and Efficiency in Machine Learning
2023 · arXiv (Cornell University)
This position paper argues that achieving robustness, privacy, and efficiency simultaneously in machine learning systems is infeasible under prevailing threat models. The tension between these goals arises not from algorithmic shortcomings but from structural limitations …