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2019

FedMD: Heterogenous Federated Learning via Model Distillation

Li, Daliang, Wang, Junpu

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Federated learning enables the creation of a powerful centralized model without compromising data privacy of multiple participants.

While successful, it does not incorporate the case where each participant independently designs its own model.
Due to intellectual property concerns and heterogeneous nature of tasks and data, this is a widespread requirement in applications of federated learning to areas such as health care and AI as a service.
In this work, we use transfer learning and knowledge distillation to develop a universal framework that enables federated learning when each agent owns not only their private data, but also uniquely designed models.

FedMD: Heterogenous Federated Learning via Model Distillation · Around

Built on

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T. Li, A. K. Sahu, M. Sanjabi, M. Zaheer, A. Talwalkar, and V. Smith, “Federated optimization for heterogeneous networks,” arXiv preprint arXiv:1812.06127
Original
2018
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WeBank AI Group, “Federated learning white paper v1.0,”
Cited in the paper.

Then

J. Kang, Z. Xiong, D. Niyato, H. Yu, Y.-C. Liang, and D. I. Kim, “Incentive design for efficient federated learning in mobile networks: A contract theory approach,” arXiv preprint arXiv:1905.07479
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L. Corinzia and J. M. Buhmann, “Variational federated multi-task learning,” arXiv preprint arXiv:1906.06268
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M. Khodak, M.-F. Balcan, and A. Talwalkar, “Adaptive gradient-based meta-learning methods,” arXiv preprint arXiv:1906.02717
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