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Federated learning (FL) enables distributed participants to collectively learn a strong global model without sacrificing their individual data privacy.
Catastrophic interference in connectionist networks: The sequential learning problem
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Federated learning: Strategies for improving communication efficiency
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Client selection for federated learning with heterogeneous resources in mobile edge
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Deep leakage from gradients
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Federated learning: Challenges, methods, and future directions
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Incentive design for efficient federated learning in mobile networks: A contract theory approach
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Federated adversarial domain adaptation
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Gradient surgery for multi-task learning
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A fixed version of quadratic program in gradient episodic memory
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