A Survey of Mobile Computing for the Visually Impaired

Weiss, Martin H.; Luck, Margaux; Girgis, Roger; Pal, Chris; Cohen, Joseph

doi:10.48550/arxiv.1811.10120

Cited by 1 publication

(1 citation statement)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…But, the proof-of-concept based solutions were not discussed. Later, Weiss et al [21] proposed a mobile application for the visually impaired with the help of ML algorithms. They have considered general object detection, specific object detection, depth and text extraction.…”

Section: Existing Surveysmentioning

confidence: 99%

Adoption of Federated Learning for Healthcare Informatics: Emerging Applications and Future Directions

Patel¹,

Bhattacharya

Tanwar

et al. 2022

IEEE Access

View full text Add to dashboard Cite

The smart healthcare system has improved the patients quality of life (QoL), where the records are being analyzed remotely by distributed stakeholders. It requires a voluminous exchange of data for disease prediction via the open communication channel, i.e., the Internet to train artificial intelligence (AI) models efficiently and effectively. The open nature of communication channels puts data privacy at high risk and affects the model training of collected data at centralized servers. To overcome this, an emerging concept, i.e., federated learning (FL) is a viable solution. It performs training at client nodes and aggregates their results to train the global model. The concept of local training preserves the privacy, confidentiality, and integrity of the patient's data which contributes effectively to the training process.The applicability of FL in the healthcare domain has various advantages, but it has not been explored to its extent. The existing surveys majorly focused on the role of FL in diverse applications, but there exists no detailed or comprehensive survey on FL in healthcare informatics (HI). We present a relative comparison of recent surveys with the proposed survey. To strengthen healthcare data privacy and increase the QoL of patients, we proposed an FL-based layered healthcare informatics architecture along with the case study on FLbased electronic health records (FL-EHR). We discuss the emerging FL models, and present the statistical and security challenges in FL adoption in medical setups. Thus, the review presents useful insights for both academia and healthcare practitioners to investigate FL application in HI ecosystems.

show abstract

Section: Existing Surveysmentioning

confidence: 99%