2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 13 , Issue 2 , PP: 202-211, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Kiran Sree Pokkuluri 1 , Ajay Kumar 2 * , Krishan Kant Singh Gautam 3 , Pratibha Deshmukh 4 , Pavithra G 5 , Laith Abualigah 6
Doi: https://doi.org/10.54216/JISIoT.130216
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.
Federated Learning , IoT Security , Centralised Learning
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