提出可独立推理的联邦学习框架，解决多方协作不可靠问题
Active-Passive Federated Learning for Vertically Partitioned Multi-view Data
- 主动客户端构建完整模型，被动客户端仅协助训练
- 模型训练后主动端可独立完成推理，无需持续协作
- 支持多种损失函数，适合跨组织长期服务场景
垂直联邦学习是整合跨设备（客户端）纵向划分的多视角数据并保护隐私的自然方法。然而，现有方法在模型推理阶段仍需所有客户端持续协作。由于推理服务可能长期运行，而实际中客户端合作（尤其来自不同组织）存在合同终止、网络中断等不确定性，可能导致服务失败。为此，我们首次提出灵活的主动-被动联邦学习（APFed）框架：主动客户端发起学习任务并负责构建完整模型，被动客户端仅作为辅助。模型构建完成后，主动客户端可独立进行推理。此外，我们将APFed实例化为两种分类方法，分别在被动客户端上引入重构损失和对比损失。通过一系列实验验证了两种方法的有效性。
原文摘要 · Abstract (English)
Vertical federated learning is a natural and elegant approach to integrate multi-view data vertically partitioned across devices (clients) while preserving their privacies. Apart from the model training, existing methods requires the collaboration of all clients in the model inference. However, the model inference is probably maintained for service in a long time, while the collaboration, especially when the clients belong to different organizations, is unpredictable in real-world scenarios, such as concellation of contract, network unavailablity, etc., resulting in the failure of them. To address this issue, we, at the first attempt, propose a flexible Active-Passive Federated learning (APFed) framework. Specifically, the active client is the initiator of a learning task and responsible to build the complete model, while the passive clients only serve as assistants. Once the model built, the active client can make inference independently. In addition, we instance the APFed framework into two classification methods with employing the reconstruction loss and the contrastive loss on passive clients, respectively. Meanwhile, the two methods are tested in a set of experiments and achieves desired results, validating their effectiveness.
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