Poisoning Attacks and Defenses in Federated Learning: A Survey

Subhash Sagar, Chang-Sun Li, Seng W. Loke, Jinho Choi
1/14/2023
cs.CRcs.LG

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Abstract

Federated learning (FL) enables the training of models among distributed clients without compromising the privacy of training datasets, while the invisibility of clients datasets and the training process poses a variety of security threats. This survey provides the taxonomy of poisoning attacks and experimental evaluation to discuss the need for robust FL.

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Cite this paper

@article{sagar2023poisoning,
  title  = {Poisoning Attacks and Defenses in Federated Learning: A Survey},
  author = {Subhash Sagar and Chang-Sun Li and Seng W. Loke and Jinho Choi},
  year   = {2023},
  eprint = {2301.05795},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2301.05795v1}
}

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