  Although Hierarchical Federated Learning (HFL) utilizes edge servers (ESs) to
alleviate communication burdens, its model performance will be degraded by
non-IID data and limited communication resources. Current works often assume
that data is uniformly distributed, which however contradicts the heterogeneity
of IoT. Solutions of additional model training to check the data distribution
inevitably increases computational costs and the risk of privacy leakage. The
challenges in solving these issues are how to reduce the impact of non-IID data
without involving raw data and how to rationalize the communication resource
allocation for addressing straggler problem. To tackle these challenges, we
propose a novel optimization method based on coaLition formation gamE and
grAdient Projection, called LEAP. Specifically, we combine edge data
distribution with coalition formation game innovatively to adjust the
correlations between clients and ESs dynamically, which ensures optimal
correlations. We further capture the client heterogeneity to achieve the
rational bandwidth allocation from coalition perception and determine the
optimal transmission power within specified delay constraints at client level.
Experimental results on four real datasets show that LEAP is able to achieve
20.62% improvement in model accuracy compared to the state-of-the-art
baselines. Moreover, LEAP effectively reduce transmission energy consumption by
at least about 2.24 times.
