Synchronization and consistency in federated learning

Develop robust, efficient synchronization and communication protocols that maintain consistent model updates and convergence across heterogeneous, intermittently connected devices in federated learning.

Background

Federated learning distributes model training across many devices without centralizing raw data. The paper explains that communication disruptions, device heterogeneity, and statistical diversity pose unique systems challenges.

Among these, ensuring that distributed devices remain in sync during training is highlighted as a core unresolved issue affecting training robustness and convergence.