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Federated Meta-Learning with Fast Convergence and Efficient Communication
[article]
2019
arXiv
pre-print
Statistical and systematic challenges in collaboratively training machine learning models across distributed networks of mobile devices have been the bottlenecks in the real-world application of federated learning. In this work, we show that meta-learning is a natural choice to handle these issues, and propose a federated meta-learning framework FedMeta, where a parameterized algorithm (or meta-learner) is shared, instead of a global model in previous approaches. We conduct an extensive
arXiv:1802.07876v2
fatcat:gpi4ck56zbcnzopiraek5jabhe