Personalized Retrogress-Resilient Federated Learning Towards Imbalanced Medical Data
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Pages (from-to) | 3663-3674 |
Number of pages | 12 |
Journal / Publication | IEEE Transactions on Medical Imaging |
Volume | 41 |
Issue number | 12 |
Online published | 19 Jul 2022 |
Publication status | Published - Dec 2022 |
Link(s)
Abstract
Clinically oriented deep learning algorithms, combined with large-scale medical datasets, have significantly promoted computer-aided diagnosis. To address increasing ethical and privacy issues, Federated Learning (FL) adopts a distributed paradigm to collaboratively train models, rather than collecting samples from multiple institutions for centralized training. Despite intensive research on FL, two major challenges are still existing when applying FL in the real-world medical scenarios, including the performance degradation (i.e., retrogress) after each communication and the intractable class imbalance. Thus, in this paper, we propose a novel personalized FL framework to tackle these two problems. For the retrogress problem, we first devise a Progressive Fourier Aggregation (PFA) at the server side to gradually integrate parameters of client models in the frequency domain. Then, at the client side, we design a Deputy-Enhanced Transfer (DET) to smoothly transfer global knowledge to the personalized local model. For the class imbalance problem, we propose the Conjoint Prototype-Aligned (CPA) loss to facilitate the balanced optimization of the FL framework. Considering the inaccessibility of private local data to other participants in FL, the CPA loss calculates the global conjoint objective based on global imbalance, and then adjusts the client-side local training through the prototype-aligned refinement to eliminate the imbalance gap with such a balanced goal. Extensive experiments are performed on real-world dermoscopic and prostate MRI FL datasets. The experimental results demonstrate the advantages of our FL framework in real-world medical scenarios, by outperforming state-of-the-art FL methods with a large margin. The source code is available at https://github.com/CityU-AIM-Group/PRR-Imbalance.
Research Area(s)
- Federated learning, retrogress, class imbalance, dermoscopic diagnosis, prostate segmentation
Citation Format(s)
Personalized Retrogress-Resilient Federated Learning Towards Imbalanced Medical Data. / Chen, Zhen; Yang, Chen; Zhu, Meilu et al.
In: IEEE Transactions on Medical Imaging, Vol. 41, No. 12, 12.2022, p. 3663-3674.
In: IEEE Transactions on Medical Imaging, Vol. 41, No. 12, 12.2022, p. 3663-3674.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review