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- Benchmarking Algorithms for Federated Domain Generalization
Prior Federated DG evaluations are limited in terms of the number or heterogeneity of clients and dataset diversity To address this gap, we propose an Federated DG benchmark methodology that enables control of the number and heterogeneity of clients and provides metrics for dataset difficulty
- Benchmarking Algorithms for Federated Domain Generalization
Thus, we propose a Federated DG benchmark that aim to test the limits of current methods with high client heterogeneity, large numbers of clients, and diverse datasets
- Benchmarking Algorithms for Federated Domain Generalization
Benchmarking Algorithms for Federated Domain Generalization While prior federated learning (FL) methods mainly consider client heterogeneity, we focus on the Federated Domain Generalization (DG) task, which introduces train-test heterogeneity in the FL context
- Benchmarking Algorithms for Federated Domain Generalization | David I . . .
Thus, we propose a Federated DG benchmark that aim to test the limits of current methods with high client heterogeneity, large numbers of clients, and diverse datasets
- Benchmarking Algorithms for Federated Domain Generalization
This paper introduces a benchmark for federated domain generalization, which is a challenging problem that requires learning a model that can generalize to heterogeneous data in a federated setting
- BENCHMARKING ALGORITHMS FOR FEDERATED DO MAIN GENERALIZATION
Domain-based client heterogeneity: While previous client heterogeneity (i e , ∃ c 6= c′, pc 6= pc′) is often expressed as label imbalance, i e , pc(y) 6= pc′(y), we make a domain-based client heterogeneity assumption that each client distribution is a (different) mixture of train domain distributions, i e , pc(x, y) = P
- Benchmarking Algorithms for Federated Domain Generalization. - dblp
We've just launched a new service: our brand new dblp SPARQL query service Read more about it in our latest blog post or try out some of the SPARQL queries linked on the dblp web pages below "Benchmarking Algorithms for Federated Domain Generalization " How can I correct errors in dblp?
- Benchmarking Algorithms for Federated Domain Generalization
While prior domain generalization (DG) benchmarks consider train-test dataset heterogeneity, we evaluate Federated DG which introduces federated learning (FL) specific challenges
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