论文标题
使用非IID数据的联合学习的逆距离聚集
Inverse Distance Aggregation for Federated Learning with Non-IID Data
论文作者
论文摘要
近年来，联邦学习（FL）是医学成像领域的一种有希望的方法。 FL中的一个关键问题，特别是在医疗方案中，要拥有一个更准确的共享模型，该模型对嘈杂和分销的客户端具有强大的态度。在这项工作中，我们解决了FL数据中统计异质性的问题，这在医学数据中是非常合理的，例如，数据来自具有不同扫描仪设置的不同站点。我们提出了IDA（反距离聚集），这是一种基于元信息的新型自适应加权方法，可处理不平衡和非IID数据。我们对众所周知的FL方法进行了广泛的分析和评估我们的方法，并将平均作为基线。
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.
