FedCTTA: 一种联邦学习中持续测试时间适应的协作方法
FedCTTA: A Collaborative Approach to Continual Test-Time Adaptation in Federated Learning
AI总结：
FedCTTA是一种隐私保护且计算高效的联邦学习中持续测试时间适应方法，通过相似性感知聚合和最小化熵提升模型适应性与可扩展性。
摘要：
联邦学习（FL）通过在分布式客户端之间协同训练模型而无需共享原始数据，使其成为隐私敏感应用的理想选择。然而，FL模型常常由于训练与部署之间的分布偏移而出现性能下降。测试时间适应（TTA）通过仅使用测试样本允许模型进行适应，提供了一个有前途的解决方案。然而，现有的FL中的TTA方法面临计算开销、特征共享带来的隐私风险以及由于内存限制导致的可扩展性问题。为了解决这些限制，我们提出了联邦持续测试时间适应（FedCTTA），一种隐私保护且计算效率高的联邦适应框架。与以往依赖共享本地特征统计的方法不同，FedCTTA通过利用模型输出分布在随机生成噪声样本上的相似性感知聚合，避免直接特征交换。这种方法在确保适应知识共享的同时保护了数据隐私。此外，FedCTTA在每个客户端最小化熵，以增强模型对演变目标分布的信心。我们的方法消除了适应过程中服务器端训练的需要，并保持了恒定的内存足迹，使其在客户端数量或训练轮次增加时仍然具有可扩展性。广泛的实验表明，FedCTTA在多样化的时空异质性场景中均优于现有方法。
英文摘要：
Federated Learning (FL) enables collaborative model training across distributed clients without sharing raw data, making it ideal for privacy-sensitive applications. However, FL models often suffer performance degradation due to distribution shifts between training and deployment. Test-Time Adaptation (TTA) offers a promising solution by allowing models to adapt using only test samples. However, existing TTA methods in FL face challenges such as computational overhead, privacy risks from feature sharing, and scalability concerns due to memory constraints. To address these limitations, we propose Federated Continual Test-Time Adaptation (FedCTTA), a privacy-preserving and computationally efficient framework for federated adaptation. Unlike prior methods that rely on sharing local feature statistics, FedCTTA avoids direct feature exchange by leveraging similarity-aware aggregation based on model output distributions over randomly generated noise samples. This approach ensures adaptive knowledge sharing while preserving data privacy. Furthermore, FedCTTA minimizes the entropy at each client for continual adaptation, enhancing the model's confidence in evolving target distributions. Our method eliminates the need for server-side training during adaptation and maintains a constant memory footprint, making it scalable even as the number of clients or training rounds increases. Extensive experiments show that FedCTTA surpasses existing methods across diverse temporal and spatial heterogeneity scenarios.
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。
