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Tag

#federated-learning

8 articles tagged #federated-learning

arxivMay 29

Collaborative Threshold Watermarking

arXiv:2602.10765v2 Announce Type: replace Abstract: In federated learning (FL), $K$ clients jointly train a model without sharing raw data. Because each participant invests data and compute, clients need mechanisms to later prove the provenance of a jointly trained model. Model watermarking embeds a

#federated-learning#model-watermarking#machine-learningRead on arxiv →
arxivMay 22bullish

Choose Wisely and Privately: Proactive Client Selection for Fair and Efficient Federated Learning

arXiv:2605.20975v2 Announce Type: replace Abstract: Federated Learning enables collaborative model training across decentralized data sources without data transfer. Averaging-based FL is limited by the presence of non-IID data, which negatively impacts convergence speed and final model accuracy. Con

#federated-learning#machine-learning#cryptographyRead on arxiv →
arxivMay 13bullish

Towards Uncertainty-Aware Federated Granger Causal Learning

arXiv:2602.13004v2 Announce Type: replace Abstract: Granger causality recovers directed interactions from time-series data, but in many distributed systems, the data are vertically partitioned across clients, with each client observing only the variables of its own subsystem. Federated Granger causa

#federated-learning#time-series#causalityRead on arxiv →
arxivMay 4bullish

Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels

arXiv:2412.00452v2 Announce Type: replace Abstract: Conventioanl federated learning (FL) heavily depends on high-quality labels, which are often impractical in the real world, leading to the federated label-noise (F-LN) problem. Worsely, the F-LN problem is exacerbated by the heterogeneity of FL, wh

#federated-learning#label-noise#machine-learningRead on arxiv →
arxivApr 30bullish

Asynchronous Federated Unlearning with Invariance Calibration for Medical Imaging

arXiv:2604.26809v1 Announce Type: new Abstract: Federated Unlearning (FU) is an emerging paradigm in Federated Learning (FL) that enables participating clients to fully remove their contributions from a trained global model, driven by data protection regulations that mandate the right to be forgotte

#federated-learning#unlearning#medical-imagingRead on arxiv →
arxivApr 24bullish

FedSIR: Spectral Client Identification and Relabeling for Federated Learning with Noisy Labels

arXiv:2604.20825v1 Announce Type: new Abstract: Federated learning (FL) enables collaborative model training without sharing raw data; however, the presence of noisy labels across distributed clients can severely degrade the learning performance. In this paper, we propose FedSIR, a multi-stage frame

FE1 model#federated-learning#noisy-labels#robust-trainingRead on arxiv →
arxivApr 22bullish

Mixture of Predefined Experts: Maximizing Data Usage on Vertical Federated Learning

arXiv:2602.12708v2 Announce Type: replace Abstract: Vertical Federated Learning (VFL) has emerged as a critical paradigm for collaborative model training in privacy-sensitive domains such as finance and healthcare. However, most existing VFL frameworks rely on the idealized assumption of full sample

SPLAVE5 models · +2#federated-learning#collaborative-model-training#privacy-sensitive-domainsRead on arxiv →
arxivApr 10bullish

Adaptive Differential Privacy for Federated Medical Image Segmentation Across Diverse Modalities

arXiv:2604.06518v1 Announce Type: cross Abstract: Large volumes of medical data remain underutilized because centralizing distributed data is often infeasible due to strict privacy regulations and institutional constraints. In addition, models trained in centralized settings frequently fail to gener

#medical-imaging#federated-learning#privacyRead on arxiv →
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