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In the growing world of artificial intelligence, federated learning is adistributed learning framework enhanced to preserve the privacy of individuals'data. Federated learning lays the groundwork for collaborative research inareas where the data is sensitive. Federated learning has several implicationsfor real-world problems. In times of crisis, when real-time decision-making iscritical, federated learning allows multiple entities to work collectivelywithout sharing sensitive data. This distributed approach enables us toleverage information from multiple sources and gain more diverse insights. Thispaper is a systematic review of the literature on privacy-preserving machinelearning in the last few years based on the Preferred Reporting Items forSystematic Reviews and Meta-Analyses (PRISMA) guidelines. Specifically, we havepresented an extensive review of supervised/unsupervised machine learningalgorithms, ensemble methods, meta-heuristic approaches, blockchain technology,and reinforcement learning used in the framework of federated learning, inaddition to an overview of federated learning applications. This paper reviewsthe literature on the components of federated learning and its applications inthe last few years. The main purpose of this work is to provide researchers andpractitioners with a comprehensive overview of federated learning from themachine learning point of view. A discussion of some open problems and futureresearch directions in federated learning is also provided.

Exploring Machine Learning Models for Federated Learning: A Review of Approaches, Performance, and Limitations