On the Convergence of Decentralized Federated Learning Under Imperfect Information Sharing

Understand

Decentralized learning and optimization is a central problem in control that encompasses several existing and emerging applications, such as federated learning.

• While there exists a vast literature on this topic and most methods centered around the celebrated average-consensus paradigm, less attention has been devoted to scenarios where the communication between the agents may be imperfect.
• To this end, this paper presents three different algorithms of Decentralized Federated Learning (DFL) in the presence of imperfect information sharing modeled as noisy communication channels.
• 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.