Inverse Distance Aggregation for Federated Learning with Non-IID Data
Yeganeh Y, Farshad A, Navab N, Albarqouni S (2020)
Publication Type: Conference contribution
Publication year: 2020
Journal
Publisher: Springer Science and Business Media Deutschland GmbH
Book Volume: 12444 LNCS
Pages Range: 150-159
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Lima, PER
ISBN: 9783030605476
DOI: 10.1007/978-3-030-60548-3_15
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.
Involved external institutions
Germany (DE)How to cite
APA:
Yeganeh, Y., Farshad, A., Navab, N., & Albarqouni, S. (2020). Inverse Distance Aggregation for Federated Learning with Non-IID Data. In Shadi Albarqouni, Spyridon Bakas, Konstantinos Kamnitsas, M. Jorge Cardoso, Bennett Landman, Wenqi Li, Fausto Milletari, Nicola Rieke, Holger Roth, Daguang Xu, Ziyue Xu (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 150-159). Lima, PER: Springer Science and Business Media Deutschland GmbH.
MLA:
Yeganeh, Yousef, et al. "Inverse Distance Aggregation for Federated Learning with Non-IID Data." Proceedings of the 2nd MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2020, and the 1st MICCAI Workshop on Distributed and Collaborative Learning, DCL 2020, held in conjunction with the Medical Image Computing and Computer Assisted Intervention, MICCAI 2020, Lima, PER Ed. Shadi Albarqouni, Spyridon Bakas, Konstantinos Kamnitsas, M. Jorge Cardoso, Bennett Landman, Wenqi Li, Fausto Milletari, Nicola Rieke, Holger Roth, Daguang Xu, Ziyue Xu, Springer Science and Business Media Deutschland GmbH, 2020. 150-159.
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