Federated Learning

Stories tagged “Federated Learning.”

5 stories

QFedPolyp: A Communication- and Inference-Efficient Federated Learning Framework for Polyp Segmentation

Researchers propose QFedPolyp, a communication- and inference-efficient federated learning framework for collaborative polyp segmentation. The framework combines quantization-aware training with low-precision model communication, reducing transmission cost and preserving …

Jul 18arxiv.org

Privacy Leakage in Federated Learning in Radiology Reports: A Comparative Evaluation of Tokenizer-Driven Privacy Risks

Researchers investigate the privacy risks of federated learning in radiology reports, focusing on the impact of tokenizer design on gradient-based text reconstruction.

Jul 18arxiv.org

Dysco: Dynamic Subspace Boosting to Mitigate LoRA Interference in Federated Learning

Federated fine-tuning of large pre-trained models increasingly relies on Low-Rank Adaptation (LoRA) to reduce communication and computation, but heterogeneous clients can make adapter aggregation unstable. This interference is controlled by the alignment between LoRA upda…

Jul 7arxiv.org

QuantFlow: A Federated Mamba-Based Post-Transformer Foundation Model for Time-Series Forecasting

Researchers introduce QuantFlow, a novel probabilistic forecasting framework that combines Mamba state-space models with federated learning for time-series prediction.

May 25arxiv.org

FederatedRSF : Federated Random Survival Forests for Partially Overlapping Medical Data

A new Python package, FederatedRSF, lets sites train survival forests without pooling patient data, even when their feature sets only partially overlap.