Flower Hub is a platform that allows researchers to publish, discover, and run federated learning (FL) benchmarks in a reproducible way. It packages benchmarks as executable, versioned applications with standardized metadata, pinned dependencies, and explicit evaluation workflows, enabling the same benchmark to run in both simulation and real deployment environments. The platform includes a multi-domain benchmark suite covering cross-silo and cross-device settings in areas such as medical imaging, finance, legal instruction tuning, phishing detection, and audio tagging, and it supports system-aware reporting of runtime and communication metrics.
arXiv:2609.15681v1 Announce Type: cross Abstract: This paper presents a systematic examination and experimental comparison of the prominent Federated Learning (FL) frameworks FedML, Flower, Substra,...
arXiv:2608. 03498v1 Announce Type: new Abstract: Federated Learning (FL) enables collaborative model training across distributed healthcare institutions without centralising sensitive patient data.
arXiv:2607. 10467v1 Announce Type: cross Abstract: Healthcare organizations often cannot freely centralize patient data because medical records are sensitive, regulated, and institutionally controlled.
FL-Net is a federated learning framework designed for medical research that addresses five key requirements identified in the literature. It offers modular data harmonization, discovery, disclosure control, versioned tools, and containerized workflow execution, enabling reuse of harmonized data and workflows across studies. The framework was evaluated on MIMIC and US-130 datasets, supporting up to 50 concurrent clients and demonstrating reproducible, audited federated workflows.
arXiv:2609.24718v1 Announce Type: new Abstract: While a centralized approach involving patient consent to collect and analyze data centrally would theoretically offer the best data quality and predic...
The paper introduces FastFederatedLearning (FFL), an open‑source federated learning framework written in C/C++ that prioritizes performance and customizability. FFL allows users to define arbitrary communication graphs between clients and servers, and it consistently outperforms Intel OpenFL with speedups of 2.5x to 3.69x across x86‑64, ARM‑v8, and RISC‑V platforms. Future plans include a Python wrapper and middleware for diverse communication backends, aiming to support dynamic, evolving federations as long‑term services.
Federated Learning (FL) enables collaborative model training across distributed healthcare institutions without centralising sensitive patient data. However, real-world healthcare federations are often characterised not only by non-IID data, but also by heterogeneous clinical objectives and partially overlapping feature spaces.
arXiv:2502. 08829v2 Announce Type: replace Abstract: Federated learning (FL) with non-IID data often degrades client performance below local training baselines.
arXiv:2608. 01095v1 Announce Type: new Abstract: Federated learning (FL) enables multiple intelligent devices to collaboratively train a high-accuracy model without sharing raw data.
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:2607. 08219v2 Announce Type: replace-cross Abstract: The privacy requirements of medical data and its substantial variations across organs and modalities hinder the clinical implementation of medical AI.
arXiv:2608. 08138v1 Announce Type: cross Abstract: Recent data protection laws have accelerated the adoption of Federated Learning (FL) for privacy-preserving decentralized training.