Federated Learning: Balancing the Thin Line Between Data Intelligence and Privacy. (arXiv:2204.13697v1 [cs.LG])

Federated learning holds great promise in learning from fragmented sensitive
data and has revolutionized how machine learning models are trained. This
article provides a systematic overview and detailed taxonomy of federated
learning. We investigate the existing security challenges in federated learning
and provide a comprehensive overview of established defense techniques for data
poisoning, inference attacks, and model poisoning attacks. The work also
presents an overview of current training challenges for federated learning,
focusing on handling non-i.i.d. data, high dimensionality issues, and
heterogeneous architecture, and discusses several solutions for the associated
challenges. Finally, we discuss the remaining challenges in managing federated
learning training and suggest focused research directions to address the open
questions. Potential candidate areas for federated learning, including IoT
ecosystem, healthcare applications, are discussed with a particular focus on
banking and financial domains.

Source: https://arxiv.org/abs/2204.13697

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