Fast and Secure Distributed Learning in High Dimension

5 May 2019El-Mahdi El-MhamdiRachid Guerraoui

Modern machine learning is distributed and the work of several machines is typically aggregated by \emph{averaging} which is the optimal rule in terms of speed, offering a speedup of $n$ (with respect to using a single machine) when $n$ processes are learning together. Distributing data and models poses however fundamental vulnerabilities, be they to software bugs, asynchrony, or worse, to malicious attackers controlling some machines or injecting misleading data in the network... (read more)

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