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Federated learning (FL) has emerged as a paradigm-shifting approach to distributed machine learning, enabling multiple participants to collaboratively train models without exposing raw data. However, ...
federated learning
differential privacy
homomorphic encryption
secure multi-party computation
Byzantine fault tolerance
zero-knowledge proofs
distributed machine learning
privacy-preserving AI
adversarial robustness
gradient privacy
