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Towards Communication-efficient Federated Learning via Sparse and Aligned Adaptive Optimization
Driven by this issue, we propose a novel sparse FedAdam algorithm called FedAdam-SSM, wherein distributed devices sparsify the updates of local model parameters and moment estimates and subsequently upload the sparse representations to the centralized server.
Nothing Wasted: Full Contribution Enforcement in Federated Edge Learning
In particular, federated edge learning (FEL) becomes prominent in securing the privacy of data owners by keeping the data locally used to train ML models.
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