Healthcare data is often split into medium/small-sized collections across
multiple hospitals and access to it is encumbered by privacy regulations. This
brings difficulties to use them for the development of machine learning and
deep learning models, which are known to be data-hungry. One way to overcome
this limitation is to use collaborative learning (CL) methods, which allow
hospitals to work collaboratively to solve a task, without the need to
explicitly share local data.
In this paper, we address a prostate segmentation problem from MRI in a
collaborative scenario by comparing two different approaches: federated
learning (FL) and consensus-based methods (CBM).
To the best of our knowledge, this is the first work in which CBM, such as
label fusion techniques, are used to solve a problem of collaborative learning.
In this setting, CBM combine predictions from locally trained models to obtain
a federated strong learner with ideally improved robustness and predictive
variance properties.
Our experiments show that, in the considered practical scenario, CBMs provide
equal or better results than FL, while being highly cost-effective. Our results
demonstrate that the consensus paradigm may represent a valid alternative to FL
for typical training tasks in medical imaging.

比较协作学习中的联邦学习和基于共识的方法，研究在医学图像领域中共识范式作为典型训练任务的替代方案的可行性。

协作学习方法成本效益评估：前列腺分割基准测试

Benchmarking Collaborative Learning Methods Cost-Effectiveness for  Prostate Segmentation

Federated learning (FL) is emerging as a new paradigm to train machine
learning models in distributed systems. Rather than sharing, and disclosing,
the training dataset with the server, the model parameters (e.g. neural
networks weights and biases) are optimized collectively by large populations of
interconnected devices, acting as local learners. FL can be applied to
power-constrained IoT devices with slow and sporadic connections. In addition,
it does not need data to be exported to third parties, preserving privacy.
Despite these benefits, a main limit of existing approaches is the centralized
optimization which relies on a server for aggregation and fusion of local
parameters; this has the drawback of a single point of failure and scaling
issues for increasing network size. The paper proposes a fully distributed (or
server-less) learning approach: the proposed FL algorithms leverage the
cooperation of devices that perform data operations inside the network by
iterating local computations and mutual interactions via consensus-based
methods. The approach lays the groundwork for integration of FL within 5G and
beyond networks characterized by decentralized connectivity and computing, with
intelligence distributed over the end-devices. The proposed methodology is
verified by experimental datasets collected inside an industrial IoT
environment.

这篇论文介绍了一种完全分布式的联邦学习算法，该算法的优点在于避免了数据披露并具有扩展性商用前景，可以在包括物联网设备在内的网络中使用。