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Papers › FACT: Federated Adversarial Cross Training

FACT: Federated Adversarial Cross Training

1 Jun 2023arXiv:2306.00607archive 2025-07-28

Stefan Schrod, Jonas Lippl, Andreas Schäfer, Michael Altenbuchinger

Federated Learning (FL) facilitates distributed model development to aggregate multiple confidential data sources. The information transfer among clients can be compromised by distributional differences, i.e., by non-i.i.d. data. A particularly challenging scenario is the federated model adaptation to a target client without access to annotated data. We propose Federated Adversarial Cross Training (FACT), which uses the implicit domain differences between source clients to identify domain shifts in the target domain. In each round of FL, FACT cross initializes a pair of source clients to generate domain specialized representations which are then used as a direct adversary to learn a domain invariant data representation. We empirically show that FACT outperforms state-of-the-art federated, non-federated and source-free domain adaptation models on three popular multi-source-single-target benchmarks, and state-of-the-art Unsupervised Domain Adaptation (UDA) models on single-source-single-target experiments. We further study FACT's behavior with respect to communication restrictions and the number of participating clients.

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Tasks

Domain AdaptationFederated LearningMulti-Source Unsupervised Domain AdaptationSource-Free Domain AdaptationUnsupervised Domain Adaptation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Domain Adaptation MNIST-to-USPS FACT Accuracy 98.8 #1 of 14 Archive leaderboard report
Domain Adaptation SVHN-to-MNIST FACT Accuracy 90.6 #10 of 14 Archive leaderboard report
Domain Adaptation USPS-to-MNIST FACT Accuracy 98.6 #2 of 14 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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