Personalized Learning with Limited Data on Edge Devices Using Federated Learning and Meta-Learning

The efficient and effective handling of few-shot learning tasks on mobile devices is challenging due to the small training set issue and the physical limitations in power and computational resources on these devices. We propose a framework that combines federated learning and meta-learning to handle independent few-shot learning tasks on multiple devices. In particular, we utilize the Prototypical Networks to perform meta-learning on all devices to learn multiple independent few-shot learning models and to aggregate the device models using federated learning which can be reused by the devices subsequently. We perform extensive experiments to (1) compare three different federated learning approaches, namely Federated Averaging (FedAvg), Federated Proximal (FedProx), and Federated Personalization (FedPer) on the proposed framework, and (2) investigate the effect of data heterogeneity issue on multiple devices on their few-shot learning performance. Our empirical results show that our proposed framework is feasible and is able to improve the devices' individual prediction performance and significant performance improvement using the aggregated model using any of the federated learning approaches when the few-shot learning tasks are from the same source and data heterogeneity continues to be a challenging issue to overcome.