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A Survey of Game-Theoretic Adversarial Learning and Its Implications on Privacy
Adversarial learning is a new and growing area of machine-learning research. Formulating it using tools from game theory allows for a different view of machine learning, when compared to the traditional, purely statistical view...
Teach: A framework for decentralized federated learning
Federated learning is a promising concept for owners of machine-learning models and owners of training data alike. We outline a framework for orchestrating federated learning and rewarding data owners that does not rely on trust or knowledge between the model owner and data owners...