  In the growing world of artificial intelligence, federated learning is a
distributed learning framework enhanced to preserve the privacy of individuals'
data. Federated learning lays the groundwork for collaborative research in
areas where the data is sensitive. Federated learning has several implications
for real-world problems. In times of crisis, when real-time decision-making is
critical, federated learning allows multiple entities to work collectively
without sharing sensitive data. This distributed approach enables us to
leverage information from multiple sources and gain more diverse insights. This
paper is a systematic review of the literature on privacy-preserving machine
learning in the last few years based on the Preferred Reporting Items for
Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Specifically, we have
presented an extensive review of supervised/unsupervised machine learning
algorithms, ensemble methods, meta-heuristic approaches, blockchain technology,
and reinforcement learning used in the framework of federated learning, in
addition to an overview of federated learning applications. This paper reviews
the literature on the components of federated learning and its applications in
the last few years. The main purpose of this work is to provide researchers and
practitioners with a comprehensive overview of federated learning from the
machine learning point of view. A discussion of some open problems and future
research directions in federated learning is also provided.
