  Federated learning (FL) has emerged as a promising paradigm for
privacy-preserving distributed machine learning, but faces challenges with
heterogeneous data distributions across clients. This paper introduces FedSat,
a novel FL approach designed to tackle various forms of data heterogeneity
simultaneously. FedSat employs a cost-sensitive loss function and a prioritized
class-based weighted aggregation scheme to address label skewness, missing
classes, and quantity skewness across clients. While the proposed
cost-sensitive loss function enhances model performance on minority classes,
the prioritized class-based weighted aggregation scheme ensures client
contributions are weighted based on both statistical significance and
performance on critical classes. Extensive experiments across diverse
data-heterogeneity settings demonstrate that FedSat significantly outperforms
state-of-the-art baselines, with an average improvement of 1.8% over the
second-best method and 19.87% over the weakest-performing baseline. The
approach also demonstrates faster convergence compared to existing methods.
These results highlight FedSat's effectiveness in addressing the challenges of
heterogeneous federated learning and its potential for real-world applications.
