偏差消除之資料增強學習於去偏差化聯邦學習

Abstract

在訓練於具有偏見數據集上的學習模型往往會觀察到類別和不良特徵之間的相關性，導致模型性能下降。大多數現有的去偏差化學習模型是為集中式機器學習而設計的，無法直接應用於保護隱私的分散式設置，如在不同客戶端收集數據的聯邦學習。為了應對具有挑戰性的去偏差化聯邦學習任務，我們提出了一種新穎的聯邦學習框架，稱為偏差消除資料增強學習（FedBEAL），該框架學習使用偏差消除資料增強器（BEA）在每個客戶端生成特定於客戶端的偏差衝突樣本。由於事先不知道偏差類型或屬性，我們提出了一種獨特的學習策略，以共同訓練BEA和提出的聯邦學習框架。我們對具有各種偏差類型的數據集進行了廣泛的圖像分類實驗，以證實FedBEAL的有效性和可應用性，在去偏差聯邦學習的性能上表現優於最先進的去偏差化方法和聯邦學習方法。

Learning models trained on biased datasets tend to observe correlations between categorical and undesirable features, which result in degraded performances. Most existing debiased learning models are designed for centralized machine learning, which cannot be directly applied to distributed settings like federated learning (FL), which collects data at distinct clients with privacy preserved. To tackle the challenging task of debiased federated learning, we present a novel FL framework of Bias-Eliminating Augmentation Learning (FedBEAL), which learns to deploy Bias-Eliminating Augmenters (BEA) for producing client-specific bias-conflicting samples at each client. Since the bias types or attributes are not known in advance, a unique learning strategy is presented to jointly train BEA with the proposed FL framework. Extensive image classification experiments on datasets with various bias types confirm the effectiveness and applicability of our FedBEAL, which performs favorably against state-of-the-art debiasing and FL methods for debiased FL.