Collaborative Machine Learning: Schemes, Robustness, and Privacy.

Distributed machine learning (ML) was originally introduced to solve a complex ML problem in a parallel way for more efficient usage of computation resources. In recent years, such learning has been extended to satisfy other objectives, namely, performing learning in situ on the training data at multiple locations and keeping the training datasets private while still allowing sharing of the model. However, these objectives have led to considerable research on the vulnerabilities of distributed learning both in terms of privacy concerns of the training data and the robustness of the learned overall model due to bad or maliciously crafted training data.

Performance Evaluation of Magnetic Resonance Coupling Method for Intra-Body Network (IBNet).

Effective management of emerging medical devices can lead to new insights in healthcare. Thus, human body communication (HBC) is becoming increasingly important. In this paper, we present magnetic resonance (MR) coupling as a promising method for the intra-body network (IBNet).