Federated Learning (FL) emerged as a significant advancement in the field of Artificial
Intelligence (AI), enabling collaborative model training across distributed devices while …

Vertical Federated Learning (VFL) enables multiple data owners, each holding a different
subset of features about largely overlapping sets of data sample (s), to jointly train a useful …

Federated learning (FL) has emerged as a promising technique for preserving user privacy
and ensuring data security in distributed machine learning contexts, particularly in edge …

Abstract Artificial intelligence employs Machine Learning (ML) and Deep Learning (DL) to
analyze data. In both, the data is stored centrally. The data involved may be sensitive and …

Abstract Fairness issues in Deep Learning models have recently received increasing
attention due to their significant societal impact. Although methods for mitigating unfairness …

Federated learning (FL) enables multiple data owners to build machine learning models
collaboratively without exposing their private local data. In order for FL to achieve …

IoT devices provide users with great convenience in smart homes. However, the
interdependent behaviors across devices may yield unexpected interactions. To analyze the …

Federated Learning (FL) enables a large number of data owners (aka FL clients) to jointly
train a machine learning model without disclosing private local data. The importance of local …

Abstract The integration of Foundation Models (FMs) with Federated Learning (FL) presents
a transformative paradigm in Artificial Intelligence (AI), offering enhanced capabilities while …

Vertical Federated Learning (VFL) enables multiple data owners, each holding a different
subset of features about a largely overlapping set of data samples, to collaboratively train a …