# SmartFLow: A Communication-Efficient SDN Framework for Cross-Silo Federated Learning

## TL;DR

SmartFLow is an SDN-based framework that dynamically optimizes network routing to significantly reduce communication latency in cross-silo federated learning, enhancing training efficiency and scalability.

## Contribution

It introduces a novel SDN-driven routing adjustment method that adapts to network conditions, improving communication efficiency in federated learning environments.

## Key findings

- Reduces synchronization time by up to 47%.
- Scales effectively to 50 clients with minimal overhead.
- Outperforms traditional routing methods in latency reduction.

## Abstract

Cross-silo Federated Learning (FL) enables multiple institutions to collaboratively train machine learning models while preserving data privacy. In such settings, clients repeatedly exchange model weights with a central server, making the overall training time highly sensitive to network performance. However, conventional routing methods often fail to prevent congestion, leading to increased communication latency and prolonged training. Software-Defined Networking (SDN), which provides centralized and programmable control over network resources, offers a promising way to address this limitation. To this end, we propose SmartFLow, an SDN-based framework designed to enhance communication efficiency in cross-silo FL. SmartFLow dynamically adjusts routing paths in response to changing network conditions, thereby reducing congestion and improving synchronization efficiency. Experimental results show that SmartFLow decreases parameter synchronization time by up to 47% compared to shortest-path routing and 41% compared to capacity-aware routing. Furthermore, it achieves these gains with minimal computational overhead and scales effectively to networks of up to 50 clients, demonstrating its practicality for real-world FL deployments.