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Federated Learning: Opportunities and Challenges

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arxiv 2101.05428 v1 pith:HIIAWTYE submitted 2021-01-14 cs.LG cs.DC

Priyanka Mary Mammen This is my paper · ORCID
classification cs.LGcs.DC
keywords learningfederatedopportunitiesprivatechallengesdatadevicesmachine
verification ladder T0 review T1 audit T2 compute T3 formal

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Federated Learning (FL) is a concept first introduced by Google in 2016, in which multiple devices collaboratively learn a machine learning model without sharing their private data under the supervision of a central server. This offers ample opportunities in critical domains such as healthcare, finance etc, where it is risky to share private user information to other organisations or devices. While FL appears to be a promising Machine Learning (ML) technique to keep the local data private, it is also vulnerable to attacks like other ML models. Given the growing interest in the FL domain, this report discusses the opportunities and challenges in federated learning.

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Cited by 17 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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