  Random forests are considered a cornerstone in machine learning for their
robustness and versatility. Despite these strengths, their conventional
centralized training is ill-suited for the modern landscape of data that is
often distributed, sensitive, and subject to privacy concerns. Federated
learning (FL) provides a compelling solution to this problem, enabling models
to be trained across a group of clients while maintaining the privacy of each
client's data. However, adapting tree-based methods like random forests to
federated settings introduces significant challenges, particularly when it
comes to non-identically distributed (non-IID) data across clients, which is a
common scenario in real-world applications. This paper presents a federated
random forest approach that employs a novel ensemble construction method aimed
at improving performance under non-IID data. Instead of growing trees
independently in each client, our approach ensures each decision tree in the
ensemble is iteratively and collectively grown across clients. To preserve the
privacy of the client's data, we confine the information stored in the leaf
nodes to the majority class label identified from the samples of the client's
local data that reach each node. This limited disclosure preserves the
confidentiality of the underlying data distribution of clients, thereby
enhancing the privacy of the federated learning process. Furthermore, our
collaborative ensemble construction strategy allows the ensemble to better
reflect the data's heterogeneity across different clients, enhancing its
performance on non-IID data, as our experimental results confirm.
