Reviewing Federated Machine Learning and Its Use in Diseases Prediction.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Data Privacy

Background:

• Machine learning (ML) enhances efficiency but faces data privacy challenges, especially in sensitive sectors like healthcare and finance.
• The "data hunger" of ML necessitates large datasets, exacerbating privacy concerns.
• Federated learning (FL) offers a solution by training models without centralizing sensitive data.

Purpose of the Study:

• To review federated learning (FL) technology and its technical underpinnings.
• To differentiate FL from traditional machine learning approaches.
• To discuss FL's applications, aggregation algorithms, and future challenges.

Main Methods:

• Review of federated learning principles and technical architecture.
• Comparison of FL with conventional machine learning data handling.
• Analysis of current FL aggregation algorithms and industry adoption trends.

Main Results:

• FL enables model training by sharing parameters, not raw data, thus preserving privacy.
• FL applications are emerging in healthcare (disease diagnosis), finance, and transportation.
• Significant market growth is projected, with increasing company implementation of FL.

Conclusions:

• Federated learning is a promising solution to ML's data privacy issues, enabling secure model training.
• FL facilitates advancements in critical areas like medical diagnosis, despite being an emerging technology.
• Addressing current limitations is key to unlocking FL's full potential and driving future innovation.