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Federated and Transfer Learning: A Survey on Adversaries and Defense Mechanisms
[article]
2022
arXiv
pre-print
The advent of federated learning has facilitated large-scale data exchange amongst machine learning models while maintaining privacy. Despite its brief history, federated learning is rapidly evolving to make wider use more practical. One of the most significant advancements in this domain is the incorporation of transfer learning into federated learning, which overcomes fundamental constraints of primary federated learning, particularly in terms of security. This chapter performs a
arXiv:2207.02337v1
fatcat:rf4fdiunnnehjpvjhbmncrt3ka