Federated Semi-supervised Learning (FedSSL) has emerged as a new paradigm for allowing distributed clients to collaboratively train a machine learning model over scarce labeled data and abundant unlabeled data. However, existing works for FedSSL rely on a closed-world assumption that all local training data and global testing data are from seen classes observed in the labeled dataset. It is crucial to go one step further: adapting FL models to an open-world setting, where unseen classes exist in the unlabeled data. In this paper, we propose a novel Federatedopen-world Semi-Supervised Learning (FedoSSL) framework, which can solve the key challenge in distributed and open-world settings, i.e., the biased training process for heterogeneously distributed unseen classes. Specifically, since the advent of a certain unseen class depends on a client basis, the locally unseen classes (exist in multiple clients) are likely to receive differentiated superior aggregation effects than the globally unseen classes (exist only in one client). We adopt an uncertainty-aware suppressed loss to alleviate the biased training between locally unseen and globally unseen classes. Besides, we enable a calibration module supplementary to the global aggregation to avoid potential conflicting knowledge transfer caused by inconsistent data distribution among different clients. The proposed FedoSSL can be easily adapted to state-of-the-art FL methods, which is also validated via extensive experiments on benchmarks and real-world datasets (CIFAR-10, CIFAR-100 and CINIC-10).

翻译：联邦半监督学习（FedSSL）作为一种新范式，允许分布式客户端在标注数据稀缺而未标注数据丰富的情况下协作训练机器学习模型。然而，现有FedSSL方法基于封闭世界假设，即所有本地训练数据和全局测试数据均来自标注数据集中已观测到的已知类别。亟需进一步突破：使联邦学习模型适应开放世界场景——即未标注数据中存在未知类别。本文提出新型联邦开放世界半监督学习（FedoSSL）框架，可解决分布式开放世界场景中的核心挑战：异构分布未知类别导致的训练偏差问题。具体而言，由于特定未知类别的出现依赖于客户端，本地未知类别（存在于多个客户端中）相较于全局未知类别（仅存在于单个客户端）更易获得差异化的聚合增益。我们采用不确定性感知抑制损失来缓解本地未知类与全局未知类之间的训练偏差。此外，我们引入校准模块作为全局聚合的补充，以避免不同客户端间数据分布不一致导致的潜在知识迁移冲突。所提出的FedoSSL可便捷适配现有最优联邦学习方法，并在基准数据集与真实场景数据集（CIFAR-10、CIFAR-100和CINIC-10）上的大量实验中得到验证。