2023 |
Kamp, Michael; Fischer, Jonas; Vreeken, Jilles Federated Learning from Small Datasets (Proceedings Article) In: International Conference on Learning Representations (ICLR), 2023. (BibTeX | Tags: black-box, black-box parallelization, daisy, daisy-chaining, FedDC, federated learning, small, small datasets) @inproceedings{kamp2023federated, |
Mian, Michael Kamp David Kaltenpoth Osman Nothing but Regrets - Privacy-Preserving Federated Causal Discovery (Proceedings Article) In: International Conference on Artificial Intelligence and Statistics (AISTATS), 2023. (BibTeX | Tags: causal discovery, causality, explainable, federated, federated causal discovery, federated learning, interpretable) @inproceedings{mian2022nothing, |
Mian, Osman; Kamp, Michael; Vreeken, Jilles Information-Theoretic Causal Discovery and Intervention Detection over Multiple Environments (Proceedings Article) In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2023. (BibTeX | Tags: causal discovery, causality, federated, federated causal discovery, federated learning, intervention) @inproceedings{mian2023informationb, |
2022 |
Michael Kamp Amr Abourayya, Erman Ayday AIMHI: Protecting Sensitive Data through Federated Co-Training (Workshop) 2022. (Links | BibTeX | Tags: aimhi, co-training, deep learning, federated learning, privacy) @workshop{abourayya2022aimhi, |
2021 |
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. (Links | BibTeX | Tags: federated learning, uncertainty) @workshop{linsner2021uncertainty, |
Li, Xiaoxiao; Jiang, Meirui; Zhang, Xiaofei; Kamp, Michael; Dou, Qi FedBN: Federated Learning on Non-IID Features via Local Batch Normalization (Proceedings Article) In: Proceedings of the 9th International Conference on Learning Representations (ICLR), 2021. (Abstract | Links | BibTeX | Tags: batch normalization, black-box parallelization, deep learning, federated learning) @inproceedings{li2021fedbn, 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. |
Publications
2023 |
Federated Learning from Small Datasets (Proceedings Article) In: International Conference on Learning Representations (ICLR), 2023. |
Nothing but Regrets - Privacy-Preserving Federated Causal Discovery (Proceedings Article) In: International Conference on Artificial Intelligence and Statistics (AISTATS), 2023. |
Information-Theoretic Causal Discovery and Intervention Detection over Multiple Environments (Proceedings Article) In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2023. |
2022 |
AIMHI: Protecting Sensitive Data through Federated Co-Training (Workshop) 2022. |
2021 |
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. |
FedBN: Federated Learning on Non-IID Features via Local Batch Normalization (Proceedings Article) In: Proceedings of the 9th International Conference on Learning Representations (ICLR), 2021. |