On-device Federated Learning with Flower
This work addresses the challenge of enabling federated learning on resource-constrained edge devices, which is incremental as it builds on existing FL frameworks.
The paper tackles the problem of poor support for on-device training in federated learning on edge devices by exploring its implementation using the Flower framework, evaluating system costs to inform more efficient algorithm designs.
Federated Learning (FL) allows edge devices to collaboratively learn a shared prediction model while keeping their training data on the device, thereby decoupling the ability to do machine learning from the need to store data in the cloud. Despite the algorithmic advancements in FL, the support for on-device training of FL algorithms on edge devices remains poor. In this paper, we present an exploration of on-device FL on various smartphones and embedded devices using the Flower framework. We also evaluate the system costs of on-device FL and discuss how this quantification could be used to design more efficient FL algorithms.