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Found 3 papers, 1 papers with code
FedNMUT -- Federated Noisy Model Update Tracking Convergence Analysis
A novel Decentralized Noisy Model Update Tracking Federated Learning algorithm (FedNMUT) is proposed that is tailored to function efficiently in the presence of noisy communication channels that reflect imperfect information exchange.
Improved Convergence Analysis and SNR Control Strategies for Federated Learning in the Presence of Noise
We propose an improved convergence analysis technique that characterizes the distributed learning paradigm of federated learning (FL) with imperfect/noisy uplink and downlink communications.
On the Convergence of Decentralized Federated Learning Under Imperfect Information Sharing
The first algorithm, Federated Noisy Decentralized Learning (FedNDL1), comes from the literature, where the noise is added to their parameters to simulate the scenario of the presence of noisy communication channels.
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