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Found 2 papers, 2 papers with code
Fast-Convergent Federated Learning via Cyclic Aggregation
Federated learning (FL) aims at optimizing a shared global model over multiple edge devices without transmitting (private) data to the central server.
Security-Preserving Federated Learning via Byzantine-Sensitive Triplet Distance
While being an effective framework of learning a shared model across multiple edge devices, federated learning (FL) is generally vulnerable to Byzantine attacks from adversarial edge devices.
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