… layered perspective, we discuss the current works and explore the challenges of designing
a vertical federated learning framework for all layers… research of vertical federated learning is …

… HybridTree [99] incorporates partyspecific knowledge by adding layers to the tree
structure, … It is the first survey to summarize related works from the perspectives of effectiveness, …

… federated source layers to unite different input features of each participant without disclosing
private data. The output of federated source layers from … trains federated source layers with …

… By taking each organization as a client, vertical federated learning is an ideal approach to …
problem of federated learning, we consider the typical vertical federated learning framework […

… on SGD-based vertical federated learning. [111] propose the … probabilistic federated neural
matching (PFNM) for multilayer … reliable federated learning: A joint optimization approach to …

… Federated learning emerges as an efficient approach to … is carried out at the FL training
layer. The FLEP is composed of a … Most studies of vertical federated learning only support two …

… Vertical Federated Learning (LVFL), targeting both computational and communication
efficiencies. This approach … Chen, “A survey on vertical federated learning: From a layered …

… surveys the latest FLIDS methods from the perspective of … improvements to vertical federated
learning still rely on … freezing the initial layers and fine-tuning the later layers using local data…

… of the main components of the DFL approach: (i) the federation nodes, detailing the evaluation
in … layers up to a certain layer (ie, the cut layer), while a specific node holds the deeper …

… output layer, whereas we take a dense vector as the output layer … We also compare our
approach to prior studies [13] … proposes one attack approach related to label reconstruction …