Federated learning provides a privacy-preserving mechanism for multiple participants to collaboratively train machine learning models without exchanging private data with each other. Existing federated learning algorithms can aggregate CNN models when the dataset is horizontally partitioned, but cannot be ...
where θq and θv are the query and vertical language models, respectively. The query language model can be estimated using the top N vertical results, (8.6)P(w∣θq)=1Z∑d∈RNP(w∣θd)P(q∣θd), where P(q∣θd) is the query likelihood score given d and Z=∑d∈RNP(q∣θd...
This chapter primarily introduces tree-based models in vertical federated learning tasks. We explain the design principles and safety verification of the federated learning algorithm in depth through the example of the random forest algorithm.#Algorithm highlights:Tree-based models in this chapter consist...
Vertical Federated Learning (VFL) allows institutions to collaborate on machine learning while keeping their original data private. VFL methods currently concentrate on collaborative training among participants with the same model architectures and use cryptography to protect training parameters. In real ...