FedNMUT -- Federated Noisy Model Update Tracking Convergence Analysis

Understand

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.

• This algorithm uses gradient tracking to minimize the impact of data heterogeneity while minimizing communication overhead.
• The proposed algorithm incorporates noise into its parameters to mimic the conditions of noisy communication channels, thereby enabling consensus among clients through a communication graph topology in such challenging environments.
• FedNMUT prioritizes parameter sharing and noise incorporation to increase the resilience of decentralized learning systems against noisy communications.