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Federated learning holds great promise in learning from fragmented sensitivedata and has revolutionized how machine learning models are trained. Thisarticle provides a systematic overview and detailed taxonomy of federatedlearning. We investigate the existing security challenges in federated learningand provide a comprehensive overview of established defense techniques for datapoisoning, inference attacks, and model poisoning attacks. The work alsopresents an overview of current training challenges for federated learning,focusing on handling non-i.i.d. data, high dimensionality issues, andheterogeneous architecture, and discusses several solutions for the associatedchallenges. Finally, we discuss the remaining challenges in managing federatedlearning training and suggest focused research directions to address the openquestions. Potential candidate areas for federated learning, including IoTecosystem, healthcare applications, are discussed with a particular focus onbanking and financial domains.

Federated Learning: Balancing the Thin Line Between Data Intelligence and Privacy