Dynamic Margin for Federated Learning with Imbalanced Data

Ran, Xinyu; Ge, Liangpeng; Zhong, Linlin

doi:10.1109/ijcnn52387.2021.9534372

Cited by 5 publications

(2 citation statements)

References 4 publications

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“…Sarkar et al [15] proposed the FL version of the focal loss which adaptively down-weights the cross-entropy loss assigned for easily classified images of the sampled collaborators from the last round. Ran et al [16] proposed the Dynamic Margin for FL method for imbalanced datasets by enlarging the margin of the local classifier by adding a dynamic term in the favor of the minority class. Zhang et al [17] offered FedSens as a FL framework to solve the challenges of imbalanced data and resource restrictions of the collaborating edge devices.…”

Section: Fl Applications On Imbalanced Datamentioning

confidence: 99%

Collaborative Colorectal Cancer Classification on Highly Class Imbalanced Data Setting via Federated Neural Style Transfer Based Data Augmentation

Nergiz¹

2022

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The deep learning algorithms achieved promising results in the computational pathology in recent decade but the high data demand of the deep learning algorithms get stuck in the multi-institutional data collaborations. The federated learning is a novel concept, which proposes to train the models of the different sites collaboratively via an orchestrating server without leaking private data. However, the imbalanced data distributions are challenging for federated learning and result in performance decrease and destabilization. In this study, the federated version of the neural style transfer algorithm, which was offered by Gatys et al. is proposed as a data augmentation method on the highly class imbalanced configuration of Chaoyang colorectal cancer imaging dataset. The proposed method works by firstly selecting characteristic style images and then generating the gram style matrices on the local sites and then transferring them to the other imbalanced sites by not leaking any private data. The proposed method contributed the ACC, F1 Score and AUC results of pure FL by 22.07%, 42.51% and 9.65% using only 20 images for content and 5 images for style. Additionally, the experiments having different content and style numbers achieved the satisfactory and consisting results.

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Section: Fl Applications On Imbalanced Datamentioning

confidence: 99%

Collaborative Colorectal Cancer Classification on Highly Class Imbalanced Data Setting via Federated Neural Style Transfer Based Data Augmentation

Nergiz¹

2022

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“…[45] propose Dynamic Margin Federated Learning (DMFL) method, which introduces a margin-based loss function to FL. The method reformu-lates non-IID FL as an imbalanced learning problem.…”

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confidence: 99%

FLightNER: A Federated Learning Approach to Lightweight Named-Entity Recognition

Abadeer¹

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We introduce FLightNER, a collaboratively-trained model in Federated Learning (FL) setting that extends an existing state-of-the-art Named-Entity Recognition (NER) model that uses prompt-tuning known as LightNER. To the best of our knowledge at the time of writing, this is the first work that adapts and evaluates prompt-tuning in an FL environment for NER. FLightNER allows the aggregation of only the trainable parameters of LightNER without model accuracy degradation and saves 10 GB per client by not aggregating the entire model parameters. Trainable-only aggregation enables more clients to join a federation without extending memory prerequisites of the central server.We evaluate our approach against two baselines using three diverse datasets with different distributions across up to seven clients in a federation. We empirically show that compared to the centrally-trained LightNER model, FLightNER outperforms it by 19% when evaluated on a medical dataset, VAERS, with label imbalance across clients. We also show that FLightNER matches the same performance of a centrallytrained LightNER on two balanced datasets: CoNLL and I2B2. Furthermore, we implement and evaluate a federated strategy that drops the outlier client at the farthest distance from the federation's average. We show that dropping the outlier client consistently enhances the performance for the majority of clients.

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Logit Calibration for Non-IID and Long-Tailed Data in Federated Learning

Wang

Shen

2022

2022 IEEE Intl Conf on Parallel &Amp; Distributed Processing With Applications, Big Data &Amp; Cloud Computing, Sustainable Com

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Dynamic Margin for Federated Learning with Imbalanced Data

Cited by 5 publications

References 4 publications

Collaborative Colorectal Cancer Classification on Highly Class Imbalanced Data Setting via Federated Neural Style Transfer Based Data Augmentation

Collaborative Colorectal Cancer Classification on Highly Class Imbalanced Data Setting via Federated Neural Style Transfer Based Data Augmentation

FLightNER: A Federated Learning Approach to Lightweight Named-Entity Recognition

Logit Calibration for Non-IID and Long-Tailed Data in Federated Learning

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