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HarmoFL: Harmonizing Local and Global Drifts in Federated Learning on Heterogeneous Medical Images. (arXiv:2112.10775v2 [eess.IV] UPDATED)
Jan. 4, 2022, 9:10 p.m. | Meirui Jiang, Zirui Wang, Qi Dou
cs.CV updates on arXiv.org arxiv.org
Multiple medical institutions collaboratively training a model using
federated learning (FL) has become a promising solution for maximizing the
potential of data-driven models, yet the non-independent and identically
distributed (non-iid) data in medical images is still an outstanding challenge
in real-world practice. The feature heterogeneity caused by diverse scanners or
protocols introduces a drift in the learning process, in both local (client)
and global (server) optimizations, which harms the convergence as well as model
performance. Many previous works have attempted …
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