解决联邦学习中特征漂移问题，提升跨设备模型性能
FedPall: Prototype-based Adversarial and Collaborative Learning for Federated Learning with Feature Drift
- 用原型对抗学习统一不同客户端的特征空间
- 通过协同学习增强特征中的类别信息，提升分类准确率
- 适合处理数据异构性强、特征分布不一致的联邦学习场景
联邦学习（FL）可在保护隐私的前提下，联合多方数据训练全局模型。然而，当各参与方使用来源不同的数据集时，数据异构性会显著降低全局模型性能，影响个性化本地模型。其中，特征漂移——即各方特征空间存在差异——在真实数据中普遍存在，但尚未得到充分研究。特征漂移会干扰客户端的特征提取学习，导致特征表示与分类性能下降。为应对联邦学习中的特征漂移问题，本文提出 FedPall 框架，利用基于原型的对抗学习统一特征空间，并通过协同学习强化特征中的类别信息。此外，FedPall 融合全局原型生成的混合特征与本地特征，从全局视角增强分类器的判别能力。在三个具有特征漂移特性的代表性数据集上的评估结果表明，FedPall 在特征漂移环境下仍能保持一致优异的分类性能。
原文摘要 · Abstract (English)
Federated learning (FL) enables collaborative training of a global model in the centralized server with data from multiple parties while preserving privacy. However, data heterogeneity can significantly degrade the performance of the global model when each party uses datasets from different sources to train a local model, thereby affecting personalized local models. Among various cases of data heterogeneity, feature drift, feature space difference among parties, is prevalent in real-life data but remains largely unexplored. Feature drift can distract feature extraction learning in clients and thus lead to poor feature extraction and classification performance. To tackle the problem of feature drift in FL, we propose FedPall, an FL framework that utilizes prototype-based adversarial learning to unify feature spaces and collaborative learning to reinforce class information within the features. Moreover, FedPall leverages mixed features generated from global prototypes and local features to enhance the global classifier with classification-relevant information from a global perspective. Evaluation results on three representative feature-drifted datasets demonstrate FedPall's consistently superior performance in classification with feature-drifted data in the FL scenario.
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