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 THIN HAIR 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 VALERIAN ROOT 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.