Corpus ID: 204578290

Federated Learning for Coalition Operations

@article{Verma2019FederatedLF,
  title={Federated Learning for Coalition Operations},
  author={D. Verma and S. Cal{\`o} and S. Witherspoon and E. Bertino and A. A. Jabal and A. Swami and G. Cirincione and S. Julier and G. White and Geeth de Mel and G. Pearson},
  journal={ArXiv},
  year={2019},
  volume={abs/1910.06799}
}
  • D. Verma, S. Calò, +8 authors G. Pearson
  • Published 2019
  • Mathematics, Computer Science
  • ArXiv
  • Machine Learning in coalition settings requires combining insights available from data assets and knowledge repositories distributed across multiple coalition partners. In tactical environments, this requires sharing the assets, knowledge and models in a bandwidth-constrained environment, while staying in conformance with the privacy, security and other applicable policies for each coalition member. Federated Machine Learning provides an approach for such sharing. In its simplest version… CONTINUE READING

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