N. Brintha

Hybrid optimization framework of federated learning of multi-models in edge networks: trade-off of energy, latency, and fairness

A new hybrid decomposition approach for optimizing multi-model federated learning (MMFL) in edge computing environments that enhances the speed of convergence, contention of resource, and real-time performance, which makes it especially appropriate to apply it to the real-world, e.g., smart healthcare, autonomous vehicles, and IoT systems.