… the Federated Forest … Federated Forest is a lossless solution for both classification and
regression problems, which achieves the same performance as the nonfederated random forest. …

… DFedForest, a federated learning system for the creation of random forests in a distributed
… proposed-system accuracy of a random forest model overcomes the results obtained when …

… paper and propose RevFRF, a revocable federated random forest (RF) framework to further
illustrate the concept. RevFRF achieves federated RF construction and prediction with the …

… methods like random forests to federated settings introduces … This paper presents a federated
random forest approach that … the privacy of the federated learning process. Furthermore, …

… We employed federated forest i in this study to build an accurate intrusion detection model.
The model built was such that (1) a partial model forest model was built and held by each …

… Our study evaluates the efficacy of federated Random Forests (FRF) models, focusing
particularly on the heterogeneity within and between datasets. We addressed three common …

… A federated version of RF has been proposed in [19], and it is proved to be as accurate as …
-federated version. In [25], authors proposed a way to create a decentralized federated forest …

… forest, and include all trees of the forest in the loss function, a gradient of the whole forest can
be computed which some/several federated … inclusion of DNDF in federated learning by an …

… The reduction in forest density has detrimental impacts on global temperatures and raises
the likelihood of forest fires. To address these challenges, this paper introduces a Federated …

… In this respect, [29] proposes using a federated forest as an intrinsically explainable … In
the federated forest, each participant builds random decision forests according to her own …