Journal of Intelligent Systems and Internet of Things JISIoT 2690-6791 2769-786X 10.54216/JISIoT https://www.americaspg.com/journals/show/3022 2019 2019 Professor & Head, Department of Computer Science and Engineering, Shri Vishnu Engineering College for Women, Bhimavaram, India admin admin Assistant Professor, Bharati Vidyapeeth (Deemed to be University) Institute of Management and Research, New Delhi, India Ajay Kumar Assistant Professor, Department of Computer Science, Shivaji College, University of Delhi, India Krishan Kant Singh Gautam University of Mumbai, Bharati Vidyapeeth’s Institute of Management and Information Technology Navi Mumbai, 400614, Maharashtra, India Pratibha Deshmukh Associate Professor, Dept. of Electronics & Communication Engineering, Dayananda Sagar College of Engineering (DSCE), Bangalore- 560078, Karnataka, India Pavithra G Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman 19328, Jordan Laith Abualigah This research compares federated and centralized learning paradigms to discover the best machine learning privacy-model accuracy balance. Federated learning allows model training across devices or clients without data centralization. It's innovative distributed machine learning. Keeping data on individual devices reduces the hazards of centralized data storage, improving user privacy and security. However, centralized learning concentrates data on a server, which raises privacy and security problems. It evaluates two learning approaches using simulated data in a simple regression problem framework. Federated learning seems to be as accurate as centralized learning while protecting privacy. The paper also shows how federated learning works in popular machine learning frameworks like TensorFlow Federated. This research shows that federated learning protects privacy while producing accurate machine learning models. It challenges the idea that machine learning must constantly choose between privacy and accuracy. Empirical facts and theoretical ideas from this study advance machine learning methodology discussions. In the digital era, it promotes privacy-conscious, dispersed learning frameworks. 2024 2024 202 211 10.54216/JISIoT.130216 https://www.americaspg.com/articleinfo/18/show/3022