Personalizing Federated Learning for Hierarchical Edge Networks with Non-IID Data
Journal:
arXiv
Published Date:
Apr 11, 2025
Abstract
Accommodating edge networks between IoT devices and the cloud server in
Hierarchical Federated Learning (HFL) enhances communication efficiency without
compromising data privacy. However, devices connected to the same edge often
share geographic or contextual similarities, leading to varying edge-level data
heterogeneity with different subsets of labels per edge, on top of device-level
heterogeneity. This hierarchical non-Independent and Identically Distributed
(non-IID) nature, which implies that each edge has its own optimization goal,
has been overlooked in HFL research. Therefore, existing edge-accommodated HFL
demonstrates inconsistent performance across edges in various hierarchical
non-IID scenarios. To ensure robust performance with diverse edge-level non-IID
data, we propose a Personalized Hierarchical Edge-enabled Federated Learning
(PHE-FL), which personalizes each edge model to perform well on the unique
class distributions specific to each edge. We evaluated PHE-FL across 4
scenarios with varying levels of edge-level non-IIDness, with extreme IoT
device level non-IIDness. To accurately assess the effectiveness of our
personalization approach, we deployed test sets on each edge server instead of
the cloud server, and used both balanced and imbalanced test sets. Extensive
experiments show that PHE-FL achieves up to 83 percent higher accuracy compared
to existing federated learning approaches that incorporate edge networks, given
the same number of training rounds. Moreover, PHE-FL exhibits improved
stability, as evidenced by reduced accuracy fluctuations relative to the
state-of-the-art FedAvg with two-level (edge and cloud) aggregation.
