Titlebook: Robust Machine Learning; Distributed Methods Rachid Guerraoui,Nirupam Gupta,Rafael Pinot Book 2024 The Editor(s) (if applicable) and The A

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2730-9908 e learning.Equips readers with skills for designing and anal.Today, machine learning algorithms are often distributed across multiple machines to leverage more computing power and more data. However, the use of a distributed framework entails a variety of security threats. In particular, some of the

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Federated Machine Learning,hat are (independently) drawn from the ground-truth distribution .. We did not, however, explicitly mention how this dataset was collected or stored. A dominant practice in machine learning for many years has been to collect and store . on a single machine, which then uses the data to train a model.

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Fundamentals of Robust Machine Learning,nt of data. The workload for each node is divided essentially by the total size of the network, while the nodes retain the control over their local data. However, the perks of federated machine learning rest upon the unrealistic assumption that each node . executes the prescribed algorithm and all i

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