Paula Raissa Silva

Abstract

Abstract
Federated learning (FL) is a collaborative, decentralized privacy-preserving method to attach the challenges of storing data and data privacy. Artificial intelligence, machine learning, smart devices, and deep learning have strongly marked the last years. Two challenges arose in data science as a result. First, the regulation protected the data by creating the General Data Protection Regulation, in which organizations are not allowed to keep or transfer data without the owner's authorization. Another challenge is the large volume of data generated in the era of big data, and keeping that data in one only server becomes increasingly tricky. Therefore, the data is allocated into different locations or generated by devices, creating the need to build models or perform calculations without transferring data to a single location. The new term FL emerged as a sub-area of machine learning that aims to solve the challenge of making distributed models with privacy considerations. This survey starts by describing relevant concepts, definitions, and methods, followed by an in-depth investigation of federated model evaluation. Finally, we discuss three promising applications for further research: anomaly detection, distributed data streams, and graph representation.This article is categorized under:Technologies > Machine LearningTechnologies > Artificial Intelligence

Abstract

Abstract
With the advances of the big data era in biology, deep learning have been incorporated in analysis pipelines trying to transform biological information into valuable knowledge. Deep learning demonstrated its power in promoting bioinformatics field including sequence analysis, bio-molecular property and function prediction, automatic medical diagnosis and to analyse cell imaging data. The ambition of this work is to create an approach that can fully explore the relationships across modalities and subjects through mining and fusing features from multi-modality data for cell state classification. The system should be able to classify cell state through multimodal deep learning techniques using heterogeneous data such as biological images, genomics and clinical annotations. Our pilot study addresses the data acquisition process and the framework capable to extract biological parameters from cell images.

Abstract