Heart Disease prediction is one of the most complicated tasks in the medical field. Day by day the cases of heart diseases are increasing at a rapid rate and it’s very important and concerning to predict any such diseases beforehand. This diagnosis is a difficult task i.e. it should be performed precisely and efficiently. We prepared a heart disease prediction system to predict whether the patient is likely to be diagnosed with heart disease or not using the medical history of the patient. Traditional approaches that involve the collection of data from devices into one centralized repository for further analysis are not always applicable due to a large amount of collected data, the use of communication channels with limited bandwidth, security and privacy requirements, etc. Federated learning (FL) is an emerging approach that allows one to analyze data directly on data sources and to federate the results of each analysis to yield a result as traditional centralized data processing. Available electronic medical records of patients quantify symptoms, body features, and clinical laboratory test values, which can be used in predicting heart disease.