In the medical field, multi-center collaborations are often sought to yield
more generalizable findings by leveraging the heterogeneity of patient and
clinical data. However, recent privacy regulations hinder the possibility to
share data, and consequently, to come up with machine learning-based solutions
that support diagnosis and prognosis. Federated learning (FL) aims at
sidestepping this limitation by bringing AI-based solutions to data owners and
only sharing local AI models, or parts thereof, that need then to be
aggregated. However, most of the existing federated learning solutions are
still at their infancy and show several shortcomings, from the lack of a
reliable and effective aggregation scheme able to retain the knowledge learned
locally to weak privacy preservation as real data may be reconstructed from
model updates. Furthermore, the majority of these approaches, especially those
dealing with medical data, relies on a centralized distributed learning
strategy that poses robustness, scalability and trust issues. In this paper we
present a federated and decentralized learning strategy, FedER, that,
exploiting experience replay and generative adversarial concepts, effectively
integrates features from local nodes, providing models able to generalize
across multiple datasets while maintaining privacy. FedER is tested on two
tasks -- tuberculosis and melanoma classification -- using multiple datasets in
order to simulate realistic non-i.i.d. medical data scenarios. Results show
that our approach achieves performance comparable to standard (non-federated)
learning and significantly outperforms state-of-the-art federated methods in
their centralized (thus, more favourable) formulation. Code is available at
this https URL

本文提出一种基于体验回放和生成对抗网络思想的联合和分散学习策略，名为 FedER，它可以在保持隐私的同时整合本地节点的特征，提供能够横跨多个数据集的模型，以实现对结核和黑色素瘤分类的准确诊断