TY - GEN
T1 - Rise of Federated Learning to Real-World Applications
AU - Prajapat, Shaligram
AU - Gehlot, Shubhi
AU - Naik, Dishita
AU - Naik, Nitin
PY - 2024/12/20
Y1 - 2024/12/20
N2 - Federated Learning (FL) emerges as a beacon of promise in addressing privacy concerns tethered to centralized machine learning. This innovative approach allows model training on diverse devices, fostering collaborative learning while zealously guarding the confidentiality of raw data. Our paper embarks on a journey through seven practical domains— Healthcare, Finance, Education, Browsing Behavior, Retail and E-Commerce, Natural Language Processing, and Recommendation Systems—revealing the transformative potential of FL without compromising data security. As we traverse these real-world landscapes, the simplicity of FL unfolds, harmonizing collaborative learning with the imperative of data privacy. This exploration beckons a future where machine learning and data-driven collaborations seamlessly navigate the realms of privacy, fostering a landscape rich with innovation and collaborative potential.
AB - Federated Learning (FL) emerges as a beacon of promise in addressing privacy concerns tethered to centralized machine learning. This innovative approach allows model training on diverse devices, fostering collaborative learning while zealously guarding the confidentiality of raw data. Our paper embarks on a journey through seven practical domains— Healthcare, Finance, Education, Browsing Behavior, Retail and E-Commerce, Natural Language Processing, and Recommendation Systems—revealing the transformative potential of FL without compromising data security. As we traverse these real-world landscapes, the simplicity of FL unfolds, harmonizing collaborative learning with the imperative of data privacy. This exploration beckons a future where machine learning and data-driven collaborations seamlessly navigate the realms of privacy, fostering a landscape rich with innovation and collaborative potential.
KW - Data Privacy
KW - Distributed Computing
KW - Federated Learning (FL)
KW - Machine Learning (ML)
UR - https://link.springer.com/chapter/10.1007/978-3-031-74443-3_41
UR - http://www.scopus.com/inward/record.url?scp=85214228714&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-74443-3_41
DO - 10.1007/978-3-031-74443-3_41
M3 - Conference publication
SN - 9783031744426
T3 - Lecture Notes in Networks and Systems
SP - 699
EP - 719
BT - Contributions Presented at The International Conference on Computing, Communication, Cybersecurity and AI, July 3–4, 2024, London, UK: The C3AI 2024
A2 - Naik, Nitin
A2 - Jenkins, Paul
A2 - Prajapat, Shaligram
A2 - Grace, Paul
ER -