Federated learning (FL) has become a prevalent distributed machine learning paradigm with improved privacy. After learning, the resulting federated model should be further personalized to each different client. While several methods have been proposed to achieve personalization, they are typically limited to a single local device, which may incur bias or overfitting since data in a single device is extremely limited. In this paper, we attempt to realize personalization beyond a single client. The motivation is that during FL, there may exist many clients with similar data distribution, and thus the personalization performance could be significantly boosted if these similar clients can cooperate with each other. Inspired by this, this paper introduces a new concept called federated adaptation, targeting at adapting the trained model in a federated manner to achieve better personalization results. However, the key challenge for federated adaptation is that we could not outsource any raw data from the client during adaptation, due to privacy concerns. In this paper, we propose PFA, a framework to accomplish Privacy-preserving Federated Adaptation. PFA leverages the sparsity property of neural networks to generate privacy-preserving representations and uses them to efficiently identify clients with similar data distributions. Based on the grouping results, PFA conducts an FL process in a group-wise way on the federated model to accomplish the adaptation. For evaluation, we manually construct several practical FL datasets based on public datasets in order to simulate both the class-imbalance and background-difference conditions. Extensive experiments on these datasets and popular model architectures demonstrate the effectiveness of PFA, outperforming other state-of-the-art methods by a large margin while ensuring user privacy. We will release our code at: https://github.com/lebyni/PFA.
Addressing Skewed Heterogeneity via Federated Prototype Rectification with Personalization
School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）；Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, China（教育部新一代人工智能技术及其交叉应用重点实验室（东南大学））
FedOBP: Federated Optimal Brain Personalization through Cloud-Edge Element-wise Decoupling
Beijing Key Laboratory of MEMS Technology and Device Reliability for Industrial Internet, the School of Intelligent Engineering and Automation, Beijing University of Posts and Telecommunications（北京工业互联网MEMS技术与器件可靠性重点实验室，智能工程与自动化学院，北京邮电大学）；School of Business Administration, Southwestern University of Finance and Economics（西南财经大学商学院）；Institute of Artificial Intelligence and the School of Computer Science, Beihang University（北京航空航天大学人工智能研究院和计算机学院）；School of Electronic Engineering, Dublin City University（都柏林城市大学电子工程学院）；DreamSoul
GRP-FED: Addressing Client Imbalance in Federated Learning via Global-Regularized Personalization
Peking University（北京大学）；Key Laboratory of Machine Perception (Ministry of Education), Peking University（北京大学机器感知重点实验室（教育部））；National Institute of Health Data Science, Peking University（北京大学健康数据科学研究院）；Institute of Medical Technology, Health Science Center of Peking University（北京大学医学部医学技术研究院）
Adaptive Expert Models for Personalization in Federated Learning
Ericsson AB（爱立信公司）；KTH Royal Institute of Technology（KTH皇家理工学院）；RISE Research Institutes of Sweden（瑞典RISE研究院）；AI Sweden（瑞典人工智能研究院）；Chalmers University of Technology（查尔姆斯理工大学）
IOP-FL: Inside-Outside Personalization for Federated Medical Image Segmentation
The Chinese University of Hong Kong（香港中文大学）
联合学习（FL）允许多个医疗机构协作学习全球模型，而无需集中客户数据。由于来自各种扫描仪和患者人口统计学的医学图像的异质性，这种全局模型很难（如果可能的话）为每个单独的客户端共同实现最佳性能。当将全局模型部署到FL之外的看不见的客户端时，这个问题变得更加严重，其中在联邦训练期间没有呈现看不见的分布。为了优化医疗成像任务的每个单独的客户端的预测精度，我们提出了一种新的统一的框架\textit{Inside and Outside model Personalization in FL}（IOP-FL）。我们的内部个性化使用了一种轻量级的基于梯度的方法，该方法通过积累用于公共知识的全局梯度和用于特定于客户端的优化的局部梯度，为每个客户端利用本地适应模型。此外，并且重要的是，所获得的局部个性化模型和全局模型可以形成多样且信息丰富的路由空间，以针对外部FL客户端个性化适配模型。因此，我们设计了一个新的测试时路由方案，使用形状约束的一致性损失，动态地将模型，给定的分布信息所传达的测试数据。我们在两个医学图像分割任务上的广泛实验结果在内部和外部个性化方面都比SOTA方法有了显着的改进，证明了我们的IOP-FL方案在临床实践中的潜力。
Adaptive Client Selection with Personalization for Communication Efficient Federated Learning
Universidade Estadual de Campinas（坎皮纳斯州立大学）；Federal University of Pará（帕拉联邦大学）；Federal University of Minas Gerais（米纳斯吉拉斯联邦大学）；Federal University of Ceará（塞阿拉联邦大学）
Evidential Trust-Aware Model Personalization in Decentralized Federated Learning for Wearable IoT
Quantum Cloud Computing and Distributed Systems (qCLOUDS) Lab（量子云计算与分布式系统实验室）；School of Computing and Information Systems（计算与信息系统学院）；The University of Melbourne, Australia（墨尔本大学）
HARMONY: Bridging the Personalization-Generalization Gap by Mitigating Representation Skew in Heterogeneous Split Federated Learning
Department of ECE and INMC, Seoul National University, Seoul, South Korea（首尔国立大学电子与信息科学系及网络信息中心，首尔，韩国）；Graduate School of Data Science, Seoul National University, Seoul, South Korea（首尔国立大学数据科学研究生院，首尔，韩国）；Department of Computer Science, University of Colorado Boulder, CO, USA（科罗拉多大学博尔德分校计算机科学系，科罗拉多州，美国）
