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Synthetic Tabular Generators Fail to Preserve Behavioral Fraud Patterns: A Benchmark on Temporal, Velocity, and Multi-Account Signals Generalization Guarantees on Data-Driven Tuning of Gradient Descent with Langevin Updates Automated co-design of high-performance thermodynamic cycles via graph-based hierarchical reinforcement learning Does Dimensionality Reduction via Random Projections Preserve Landscape Features? 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Poisoning Attacks and Defenses in Federated Learning: A Survey
Subhash Sagar, Chang-Sun Li, Seng W. Loke, Jinho Choi · 2023-01-14 · via cs.LG updates on arXiv.org

Federated learning (FL) enables the training of models among distributed clients without compromising the privacy of training datasets, while the invisibility of clients datasets and the training process poses a variety of security threats. This survey provides the taxonomy of poisoning attacks and experimental evaluation to discuss the need for robust FL.

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