Nowadays, Federated learning (FL) has surfaced as an auspicious approach that allows collaborative machine learning in smart healthcare systems without compromising data privacy. FL has incredible advantages, but still there are a number of security and privacy issues that need to be resolved before it can be used in smart healthcare settings in a safe and competent manner. The security and privacy concerns of FL technology in smart healthcare systems are thoroughly reviewed in this chapter. This chapter will present a careful explanation of some remarkable problems that FL presents, such as data privacy, communication security, model poisoning, and the reliability of involved parties. We also examine current ideas and plans put forth to lessen these difficulties, including blockchain-based methods, safe aggregation, and differential privacy. To further strengthen the security and privacy of FL in smart healthcare systems, we also outline future research topics and possible areas for improvement. Additionally, a comparative analysis is mentioned in this chapter, which clearly differentiates between this chapter’s contribution and existing research work on this area. Researchers, medical professionals, and legislators may all benefit from this work’s insightful analysis of the security and privacy concerns associated with FL and its potential solutions for smart healthcare settings.