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We address the relatively unexplored problem of hyper-parameter optimization (HPO) for federated learning (FL-HPO).
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Earlier work this paper cites.
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Christopher K Williams and Carl Edward Rasmussen · 2006
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Sequential model-based optimization for general algorithm configuration
Frank Hutter, Holger H Hoos, and Kevin Leyton-Brown · 2011
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
Adam: A method for stochastic optimization
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Earlier work this paper cites.
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Peter Kairouz, H Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al · 2019
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Mikhail Khodak, Tian Li, Liam Li, M Balcan, Virginia Smith, and Ameet Talwalkar · 2020
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Communication-efficient learning of deep networks from decentralized data
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Differentially private federated bayesian optimization with distributed exploration
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Federated hyperparameter tuning: Challenges, baselines, and connections to weight-sharing
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