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We consider learning a multi-class classification model in the federated setting, where each user has access to the positive data associated with only a single class.
Error-correcting output codes: A general method for improving multiclass inductive learning programs
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Federated learning: Challenges, methods, and future directions
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Practical secure aggregation for federated learning on user-held data
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Agnostic federated learning
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Stochastic negative mining for learning with large output spaces
Reddi, S. J., Kale, S., Yu, F., Holtmann-Rice, D., Chen, J., and Kumar, S · 2019
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