FedPS is a federated preprocessing framework that uses aggregated statistics to address missing values, inconsistent formats, and heterogeneous feature scales in structured data. It employs data-sketching techniques to summarize local datasets efficiently, enabling federated algorithms for feature scaling, encoding, discretization, and missing-value imputation. The framework also extends preprocessing-related models, such as Bayesian Linear Regression, to both horizontal and vertical federated learning settings, offering communication‑efficient and consistent pipelines for practical deployments.
arXiv:2608. 14654v1 Announce Type: cross Abstract: Federated Learning (FL) is a collaborative paradigm that enables multiple devices to train a global model while preserving local data privacy.
arXiv:2606. 16891v1 Announce Type: cross Abstract: Federated Learning is rapidly evolving beyond the exchange of traditional model weights and gradients, yet existing definitions fail to capture the full scope of modern payloads like synthetic data and federated analytics.
arXiv:2606. 31742v1 Announce Type: cross Abstract: Explainable AI (XAI) methods have demonstrated significant success in recent years at identifying relevant features in input data that drive deep learning model decisions, enhancing interpretability for users.
The paper proposes a new federated learning approach called FedALS that reduces communication costs by varying aggregation frequencies across model layers. It derives tighter generalization bounds for one‑round and multi‑round federated learning, linking these bounds to local updates and data heterogeneity. Based on representation‑learning insights, the authors argue that infrequent aggregation of early layers and more frequent aggregation of final layers yields more generalizable models, especially in non‑iid settings, and demonstrate the method’s effectiveness experimentally.
arXiv:2606. 14416v1 Announce Type: new Abstract: Federated learning (FL) often struggles with generalization due to heterogeneous client data.
arXiv:2608. 12108v1 Announce Type: new Abstract: Federated learning (FL) enables collaborative model training across distributed clients while keeping data local.
arXiv:2409. 15723v3 Announce Type: replace Abstract: Large Language Models have achieved impressive performance across diverse applications, yet their training typically depends on centralized data collection, raising serious privacy and governance concerns.
Fed-ReMasker is a federated learning approach that adapts the ReMasker masked autoencoder for tabular data imputation, specifically addressing feature-level missingness where entire features are absent at some centers. The method enables centers to impute unobserved features by leveraging knowledge from collaborating institutions. In benchmark tests on synthetic and real-world datasets, Fed-ReMasker achieves the lowest imputation error in the majority of scenarios and remains robust to client heterogeneity, closely matching the performance of a centralized model.
arXiv:2609.22654v1 Announce Type: cross Abstract: Federated learning (FL) has emerged as a leading privacy-preserving framework for collaborative machine learning across decentralized environments. W...
arXiv:2606. 26037v1 Announce Type: cross Abstract: Federated learning has emerged as the foremost approach for decentralized model training with privacy preservation.
Influence-Oriented Personalized Federated Learning (FedC^2I) introduces a framework that quantifies both client-level and class-level influence to enable adaptive parameter aggregation in federated learning. By modeling inter-client influence through influence vectors and matrices, FedC^2I allows clients to selectively acquire knowledge from similar peers and guides the aggregation of feature representations and classifiers. Experiments under non-IID settings show that FedC^2I outperforms existing federated learning methods in effectiveness, robustness, and interpretability.
Federated Learning (FL) enables collaborative model training across distributed client devices while preserving data privacy. However, FL faces significant challenges due to data heterogeneity, particularly in terms of label distribution skewness and variations in dataset sizes, which can lead to biased model updates and hinder convergence.