We propose an end-to-end algorithm that puts in coalescence the mechanism of learning collaboratively in a decentralized fashion, using Federated Learning, while preserving differential privacy of each participating client, which are typically conceived as resource-constrained edge devices. We have developed the proposed infrastructure and analyzed its performance from the standpoint of a machine learning task using standard metrics.
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