Topology-Driven Synchronization Interval Optimization for Latency-Constrained Geo-Decentralized Federated Learning

Geo-decentralized federated learning (FL) can empower fully distributed model training for future large-scale 6G networks. Without the centralized parameter server, the peer-to-peer model synchronization in geo-decentralized FL would incur excessive communication overhead. Some existing studies optimized synchronization interval for communication efficiency, but may not be applicable to latency-constrained geo-decentralized FL. This paper first proposes the synchronization interval optimization for latency-constrained geo-decentralized FL. The problem is formulated to maximize the model training accuracy within a time window under communication/computation constraints. We mathematically derive the convergence bound by jointly considering data heterogeneity, network topology and communication/computation resources. By minimizing the convergence bound, we optimize the synchronization interval based on the approximated system consistency metric. Extensive experiments on MNIST, Fashion-MNIST and CIFAR10 datasets validate the superiority of the proposed approach by achieving up to 30% higher accuracy than the state-of-the-art benchmarks.