Connectivity-Aware Semi-Decentralized Federated Learning over Time-Varying D2D Networks

Understand

Semi-decentralized federated learning blends the conventional device to-server (D2S) interaction structure of federated model training with localized device-to-device (D2D) communications.

• We study this architecture over practical edge networks with multiple D2D clusters modeled as time-varying and directed communication graphs.
• Our investigation results in an algorithm that controls the fundamental trade-off between (a) the rate of convergence of the model training process towards the global optimizer, and (b) the number of D2S transmissions required for global aggregation.
• Specifically, in our semi-decentralized methodology, D2D consensus updates are injected into the federated averaging framework based on column-stochastic weight matrices that encapsulate the connectivity within the clusters.