5 discusses two attacks that target weaknesses of federated learning systems: (1) data leakage, i.e., inferring raw data used to train an AI model by unauthorized parties, and (2) data poisoning, i.e., a cyberattack that compromises data used to train an AI model to manipulate its output.
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5 discusses two attacks that target weaknesses of federated learning systems: (1) data leakage, i.e., inferring raw data used to train an AI model by unauthorized parties, and (2) data poisoning, i.e., a cyberattack that compromises data used to train an AI model to manipulate its output.
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Add this copy of Federated Learning: A Primer for Mathematicians to cart. £129.64, new condition, Sold by Ingram Customer Returns Center rated 5.0 out of 5 stars, ships from NV, USA, published 2025 by Springer Nature Switzerland AG.
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New. Contains: Illustrations, black & white, Illustrations, color. ICIAM2023 Springer Series . XIV, 82 p. 19 illus., 18 illus. in color. Intended for professional and scholarly audience.
