SemiFL：半监督的联邦学习为无标签的客户端与交替培训
SemiFL: Semi-Supervised Federated Learning for Unlabeled Clients with Alternate Training
摘要：
联邦学习允许通过使用许多分布式客户端的计算和私有数据资源来训练机器学习模型。联邦学习（FL）的大多数现有结果假设客户端具有地面真实标签。然而，在许多实际场景中，由于缺乏专业知识或资源，客户端可能无法标记特定于任务的数据。我们提出SemiFL来解决将FedAvg等通信效率高的FL与半监督学习（SSL）相结合的问题。在SemiFL中，客户端具有完全未标记的数据，并且可以训练多个本地epoch以降低通信成本，而服务器具有少量的标记数据。我们提供了一个成功的数据增强为基础的SSL方法的理论理解，以说明一个香草组合的通信效率FL与SSL的瓶颈。为了解决这个问题，我们提出了替代训练，以“用标记数据微调全局模型”和“用全局模型生成伪标签”。“我们进行了广泛的实验，并证明我们的方法显着提高了标记服务器的性能，而未标记客户端使用多个本地时期进行训练。此外，我们的方法优于许多现有的SSFL基线，并与最先进的FL和SSL结果竞争。
英文摘要：
Federated Learning allows the training of machine learning models by using the computation and private data resources of many distributed clients. Most existing results on Federated Learning (FL) assume the clients have ground-truth labels. However, in many practical scenarios, clients may be unable to label task-specific data due to a lack of expertise or resource. We propose SemiFL to address the problem of combining communication-efficient FL such as FedAvg with Semi-Supervised Learning (SSL). In SemiFL, clients have completely unlabeled data and can train multiple local epochs to reduce communication costs, while the server has a small amount of labeled data. We provide a theoretical understanding of the success of data augmentation-based SSL methods to illustrate the bottleneck of a vanilla combination of communication-efficient FL with SSL. To address this issue, we propose alternate training to `fine-tune global model with labeled data' and `generate pseudo-labels with the global model.' We conduct extensive experiments and demonstrate that our approach significantly improves the performance of a labeled server with unlabeled clients training with multiple local epochs. Moreover, our method outperforms many existing SSFL baselines and performs competitively with the state-of-the-art FL and SSL results.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
