Federated Learning: Challenges and Barriers to Widespread Adoption in the AI Landscape
Author(s): Vishakha Agrawal
Publication #: 2501041
Date of Publication: 07.12.2021
Country: USA
Pages: 1-4
Published In: Volume 7 Issue 6 December-2021
DOI: https://doi.org/10.5281/zenodo.14755519
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
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