Federated Learning: Challenges and Barriers to Widespread Adoption in the AI Landscape

Authors

Vishakha Agrawal

Abstract

Federated Learning (FL) has emerged as a promis- ing paradigm for distributed machine learning that addresses privacy concerns by enabling model training on decentralized data. Despite its potential benefits, FL faces several significant challenges that have hindered its widespread adoption in practical applications. This paper examines the technical, organizational, and systemic barriers to FL implementation and proposes po- tential solutions to accelerate its adoption in the AI ecosystem.

Keywords

Federated Learning, Distributed ML, Hetero- geneity, Compliance, non-IID

Federated Learning: Challenges and Barriers to Widespread Adoption in the AI Landscape. Vishakha Agrawal. 2021. IJIRCT, Volume 7, Issue 6. Pages 1-4. https://www.ijirct.org/viewPaper.php?paperId=2501041