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Federated Learning with Heterogeneous and Private Label Sets
Adam Breitholtz, Edvin Listo Zec, Fredrik D. Johansson
Although common in real-world applications, heterogeneous client label sets are rarely investigated in federated learning (FL). Furthermore, in the cases they are, clients are assu…
Overcoming label shift with target-aware federated learning
Edvin Listo Zec, Adam Breitholtz, Fredrik D. Johansson
Federated learning enables multiple actors to collaboratively train models without sharing private data. Existing algorithms are successful and well-justified in this task when the…
Practicality of generalization guarantees for unsupervised domain adaptation with neural networks
Adam Breitholtz, Fredrik D. Johansson
Understanding generalization is crucial to confidently engineer and deploy machine learning models, especially when deployment implies a shift in the data domain. For such domain a…
Unsupervised domain adaptation by learning using privileged information
Adam Breitholtz, Anton Matsson, Fredrik D. Johansson
Successful unsupervised domain adaptation is guaranteed only under strong assumptions such as covariate shift and overlap between input domains. The latter is often violated in hig…