Harmonia: Accurate Federated Learning with All-Inclusive Dataset

Wonmi Choi, Juyoung Ahn, Yeonho Yoo, Chuck Yoo, Gyeongsik Yang

Abstract: Federated learning (FL) is an appealing model training technique that utilizes heterogeneous datasets and user devices, ensuring user data privacy. Existing FL research proposed device selection schemes to balance the computing speeds of devices. However, we observe that these schemes compromise prediction accuracy by ~57. 7 %. To solve this problem, we present Harmonia that enhances prediction accuracy, while also balancing the diverse computing speeds of devices. Our evaluation shows that Harmonia improves prediction accuracy by ~ 1.7 x over existing schemes.