2023
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Kamp, Michael; Fischer, Jonas; Vreeken, Jilles Federated Learning from Small Datasets (Inproceedings) In: International Conference on Learning Representations (ICLR), 2023. @inproceedings{kamp2023federated,
title = {Federated Learning from Small Datasets},
author = {Michael Kamp and Jonas Fischer and Jilles Vreeken},
year = {2023},
date = {2023-05-01},
urldate = {2023-05-01},
booktitle = {International Conference on Learning Representations (ICLR)},
journal = {arXiv preprint arXiv:2110.03469},
keywords = {black-box, black-box parallelization, daisy, daisy-chaining, FedDC, federated learning, small, small datasets},
pubstate = {published},
tppubtype = {inproceedings}
}
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Mian, Michael Kamp David Kaltenpoth Osman Nothing but Regrets - Privacy-Preserving Federated Causal Discovery (Inproceedings) In: International Conference on Artificial Intelligence and Statistics (AISTATS), 2023. @inproceedings{mian2022nothing,
title = {Nothing but Regrets - Privacy-Preserving Federated Causal Discovery},
author = {Michael Kamp David Kaltenpoth Osman Mian},
year = {2023},
date = {2023-04-25},
urldate = {2023-04-25},
booktitle = {International Conference on Artificial Intelligence and Statistics (AISTATS)},
keywords = {causal discovery, causality, explainable, federated, federated causal discovery, federated learning, interpretable},
pubstate = {published},
tppubtype = {inproceedings}
}
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Mian, Osman; Kamp, Michael; Vreeken, Jilles Information-Theoretic Causal Discovery and Intervention Detection over Multiple Environments (Inproceedings) In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2023. @inproceedings{mian2023informationb,
title = {Information-Theoretic Causal Discovery and Intervention Detection over Multiple Environments},
author = {Osman Mian and Michael Kamp and Jilles Vreeken},
year = {2023},
date = {2023-02-07},
urldate = {2023-02-07},
booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)},
keywords = {causal discovery, causality, federated, federated causal discovery, federated learning, intervention},
pubstate = {published},
tppubtype = {inproceedings}
}
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2022
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Michael Kamp Amr Abourayya, Erman Ayday AIMHI: Protecting Sensitive Data through Federated Co-Training (Workshop) 2022. @workshop{abourayya2022aimhi,
title = {AIMHI: Protecting Sensitive Data through Federated Co-Training},
author = {Amr Abourayya, Michael Kamp, Erman Ayday, Jens Kleesiek, Kanishka Rao, Geoffrey I. Webb, Bharat Rao},
url = {http://trustworthyml.de/wp-content/uploads/2022/12/45_aimhi_protecting_sensitive_dat.pdf},
year = {2022},
date = {2022-12-02},
urldate = {2022-12-02},
howpublished = {FL-NeurIPS22},
keywords = {aimhi, co-training, deep learning, federated learning, privacy},
pubstate = {published},
tppubtype = {workshop}
}
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Mian, Osman; Kaltenpoth, David; Kamp, Michael Regret-based Federated Causal Discovery (Inproceedings) In: The KDD'22 Workshop on Causal Discovery, pp. 61–69, PMLR 2022. @inproceedings{mian2022regret,
title = {Regret-based Federated Causal Discovery},
author = {Osman Mian and David Kaltenpoth and Michael Kamp},
year = {2022},
date = {2022-01-01},
booktitle = {The KDD'22 Workshop on Causal Discovery},
pages = {61--69},
organization = {PMLR},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Li, Jianning; Ferreira, André; Puladi, Behrus; Alves, Victor; Kamp, Michael; Kim, Moon-Sung; Nensa, Felix; Kleesiek, Jens; Ahmadi, Seyed-Ahmad; Egger, Jan Open-Source Skull Reconstruction with MONAI (Journal Article) In: arXiv preprint arXiv:2211.14051, 2022. @article{li2022open,
title = {Open-Source Skull Reconstruction with MONAI},
author = {Jianning Li and Andr\'{e} Ferreira and Behrus Puladi and Victor Alves and Michael Kamp and Moon-Sung Kim and Felix Nensa and Jens Kleesiek and Seyed-Ahmad Ahmadi and Jan Egger},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {arXiv preprint arXiv:2211.14051},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
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Wang, Junhong; Li, Yun; Zhou, Zhaoyu; Wang, Chengshun; Hou, Yijie; Zhang, Li; Xue, Xiangyang; Kamp, Michael; Zhang, Xiaolong; Chen, Siming When, Where and How does it fail? A Spatial-temporal Visual Analytics Approach for Interpretable Object Detection in Autonomous Driving (Journal Article) In: IEEE Transactions on Visualization and Computer Graphics, 2022. @article{wang2022and,
title = {When, Where and How does it fail? A Spatial-temporal Visual Analytics Approach for Interpretable Object Detection in Autonomous Driving},
author = {Junhong Wang and Yun Li and Zhaoyu Zhou and Chengshun Wang and Yijie Hou and Li Zhang and Xiangyang Xue and Michael Kamp and Xiaolong Zhang and Siming Chen},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {IEEE Transactions on Visualization and Computer Graphics},
publisher = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Mian, Osman; Kaltenpoth, David; Kamp, Michael Regret-based Federated Causal Discovery (Inproceedings) In: The KDD'22 Workshop on Causal Discovery, pp. 61–69, PMLR 2022. @inproceedings{mian2022regretb,
title = {Regret-based Federated Causal Discovery},
author = {Osman Mian and David Kaltenpoth and Michael Kamp},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
booktitle = {The KDD'22 Workshop on Causal Discovery},
pages = {61--69},
organization = {PMLR},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
2021
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Linsner, Florian; Adilova, Linara; Däubener, Sina; Kamp, Michael; Fischer, Asja Approaches to Uncertainty Quantification in Federated Deep Learning (Workshop) Machine Learning and Principles and Practice of Knowledge Discovery in Databases: International Workshops of ECML PKDD 2021, vol. 2, Springer, 2021. @workshop{linsner2021uncertainty,
title = {Approaches to Uncertainty Quantification in Federated Deep Learning},
author = {Florian Linsner and Linara Adilova and Sina D\"{a}ubener and Michael Kamp and Asja Fischer},
url = {https://michaelkamp.org/wp-content/uploads/2022/04/federatedUncertainty.pdf},
year = {2021},
date = {2021-09-17},
urldate = {2021-09-17},
booktitle = {Machine Learning and Principles and Practice of Knowledge Discovery in Databases: International Workshops of ECML PKDD 2021},
issuetitle = {Workshop on Parallel, Distributed, and Federated Learning},
volume = {2},
pages = {128-145},
publisher = {Springer},
keywords = {federated learning, uncertainty},
pubstate = {published},
tppubtype = {workshop}
}
|
Li, Xiaoxiao; Jiang, Meirui; Zhang, Xiaofei; Kamp, Michael; Dou, Qi FedBN: Federated Learning on Non-IID Features via Local Batch Normalization (Inproceedings) In: Proceedings of the 9th International Conference on Learning Representations (ICLR), 2021. @inproceedings{li2021fedbn,
title = {FedBN: Federated Learning on Non-IID Features via Local Batch Normalization},
author = {Xiaoxiao Li and Meirui Jiang and Xiaofei Zhang and Michael Kamp and Qi Dou},
url = {https://michaelkamp.org/wp-content/uploads/2021/05/fedbn_federated_learning_on_non_iid_features_via_local_batch_normalization.pdf
https://michaelkamp.org/wp-content/uploads/2021/05/FedBN_appendix.pdf},
year = {2021},
date = {2021-05-03},
urldate = {2021-05-03},
booktitle = {Proceedings of the 9th International Conference on Learning Representations (ICLR)},
abstract = {The emerging paradigm of federated learning (FL) strives to enable collaborative training of deep models on the network edge without centrally aggregating raw data and hence improving data privacy. In most cases, the assumption of independent and identically distributed samples across local clients does not hold for federated learning setups. Under this setting, neural network training performance may vary significantly according to the data distribution and even hurt training convergence. Most of the previous work has focused on a difference in the distribution of labels or client shifts. Unlike those settings, we address an important problem of FL, e.g., different scanners/sensors in medical imaging, different scenery distribution in autonomous driving (highway vs. city), where local clients store examples with different distributions compared to other clients, which we denote as feature shift non-iid. In this work, we propose an effective method that uses local batch normalization to alleviate the feature shift before averaging models. The resulting scheme, called FedBN, outperforms both classical FedAvg, as well as the state-of-the-art for non-iid data (FedProx) on our extensive experiments. These empirical results are supported by a convergence analysis that shows in a simplified setting that FedBN has a faster convergence rate than FedAvg. Code is available at https://github.com/med-air/FedBN.},
keywords = {batch normalization, black-box parallelization, deep learning, federated learning},
pubstate = {published},
tppubtype = {inproceedings}
}
The emerging paradigm of federated learning (FL) strives to enable collaborative training of deep models on the network edge without centrally aggregating raw data and hence improving data privacy. In most cases, the assumption of independent and identically distributed samples across local clients does not hold for federated learning setups. Under this setting, neural network training performance may vary significantly according to the data distribution and even hurt training convergence. Most of the previous work has focused on a difference in the distribution of labels or client shifts. Unlike those settings, we address an important problem of FL, e.g., different scanners/sensors in medical imaging, different scenery distribution in autonomous driving (highway vs. city), where local clients store examples with different distributions compared to other clients, which we denote as feature shift non-iid. In this work, we propose an effective method that uses local batch normalization to alleviate the feature shift before averaging models. The resulting scheme, called FedBN, outperforms both classical FedAvg, as well as the state-of-the-art for non-iid data (FedProx) on our extensive experiments. These empirical results are supported by a convergence analysis that shows in a simplified setting that FedBN has a faster convergence rate than FedAvg. Code is available at https://github.com/med-air/FedBN. |
Petzka, Henning; Kamp, Michael; Adilova, Linara; Sminchisescu, Cristian; Boley, Mario Relative flatness and generalization (Journal Article) In: Advances in Neural Information Processing Systems, vol. 34, pp. 18420–18432, 2021. @article{petzka2021relative,
title = {Relative flatness and generalization},
author = {Henning Petzka and Michael Kamp and Linara Adilova and Cristian Sminchisescu and Mario Boley},
year = {2021},
date = {2021-01-01},
journal = {Advances in Neural Information Processing Systems},
volume = {34},
pages = {18420--18432},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|