ERAFL: Efficient Resource Allocation for Federated Learning Training in Smart Homes

With the growing number of Federated Learning (FL) applications in smart homes, it becomes crucial to manage communication and computation resources within the smart home so that FL applications can complete their training on time. While computation offloading has relieved the challenge of timely completion of applications in case of high competition for local resources, privacy of the smart home data remains a critical concern. This paper introduces ERAFL, a resource allocation and computation offloading algorithm running on a home gateway. Unlike privacy-oblivious prior works, ERAFL considers privacy-sensitivity level of FL training data in offloading decision, prioritizing local processing of more sensitive data, e.g., biological personal data. Moreover, in case of insufficient local resources, ERAFL offloads a part of data and accelerates training by leveraging parallel training on the cloud and the edge device. It also imposes limits on the amount of offloaded data or performs the training either locally or remotely to ensure model accuracy. Our simulation results show that ERAFL can satisfy more FL training tasks and reduce data privacy leakage in comparison to the baselines that do not consider partial offloading, privacy sensitivity of application data or resource allocation.