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Found 2 papers, 1 papers with code
Communication-Efficient Heterogeneous Federated Learning with Generalized Heavy-Ball Momentum
Federated Learning (FL) is the state-of-the-art approach for learning from decentralized data in privacy-constrained scenarios.
Speeding up Heterogeneous Federated Learning with Sequentially Trained Superclients
data severely impairs both the performance of the trained neural network and its convergence rate, increasing the number of communication rounds requested to reach a performance comparable to that of the centralized scenario.
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