Abstract
Federated learning (FL) has been a promising approach in the field of medical imaging in recent years. A critical problem in FL, specifically in medical scenarios is to have a more accurate shared model which is robust to noisy and out-of distribution clients. In this work, we tackle the problem of statistical heterogeneity in data for FL which is highly plausible in medical data where for example the data comes from different sites with different scanner settings. We propose IDA (Inverse Distance Aggregation), a novel adaptive weighting approach for clients based on meta-information which handles unbalanced and non-iid data. We extensively analyze and evaluate our method against the well-known FL approach, Federated Averaging as a baseline.
Project page: https://ida-fl.github.io/.
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Acknowledgements
S.A. is supported by the PRIME programme of the German Academic Exchange Service (DAAD) with funds from the German Federal Ministry of Education and Research (BMBF). A.F. is supported by Munich Center for Machine Learning (MCML) with funding from the German Federal Ministry of Education and Research (BMBF) under Grant No. 01IS18036B. We also gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan V GPU used for this research.
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Yeganeh, Y., Farshad, A., Navab, N., Albarqouni, S. (2020). Inverse Distance Aggregation for Federated Learning with Non-IID Data. In: Albarqouni, S., et al. Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning. DART DCL 2020 2020. Lecture Notes in Computer Science(), vol 12444. Springer, Cham. https://doi.org/10.1007/978-3-030-60548-3_15
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