Federated learning (FL) enables clients to collaboratively train a model, while keeping their
local training data decentralized. However, high communication costs, data heterogeneity …

With the continuous expansion of the application scope of computer software, the standards
of software quality of various organizations are becoming more and more strict. As a testing …

Quality patient care is a complex and multifaceted problem requiring the integration of data
from multiple sources. We propose Medicient, a knowledge-graph-based question …

Machine learning models benefit from large and diverse training datasets. However, it is
difficult for an individual organization to collect sufficiently diverse data. Additionally, the …

In this chapter, we discuss the safety and robustness of transfer learning. By safety, we refer
to its defense and solutions against attack and data privacy misuse. By robustness, we mean …

Personalized Federated Learning (FL) handles the data heterogeneous problem by tailoring
local models for each distributed data owner. Previous studies first train a highly-adaptable …

Federated learning aims at building machine learning models without compromising data
privacy from the clients. Since different clients naturally have different data distributions (ie …

A promising approach to address privacy concerns, Federated learning (FL) enables
distributed training of machine learning (ML) models where user data remains on edge …