Flower

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Flower: A Friendly Federated Learning Framework Flower offers a unified approach to federated learning. You can federate any workload, any ML framework, and ...

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Scaling Flower Al for Enterprise through Open Cluster Management

Flower Health

PaLA A Single-Model Alt to Clustering in FL under Group-Specific Drift

Panel Sovereign AI and Arrival of European NeoLabs

F-DRL Federated Dynamics Representation Learning for Robust MultiTask Reinforcement Learning

NeuroFL OBI’s Intelligence Network for Brain Health Data

Flower Agents SuperGrid for Secure Collaborative AI Agents

FLIP Open-Source Federated Learning Interoperability Platform for Collaborative Al in Healthcare

Party Responsibly Governing Cross-Platform Collaborative AI

+250.000 Patients. Zero data leaving.

Federated Learning in Critical Infrastructure

A Resource-Aware Federated Learning Framework for Networks of Constrained Devices

Federated Learning in Trusted Research Environments

Why we need Federated Finetuning for AI Native Networks

Fireside Chat with Eric Boernert

Fireside Chat with Gabriele Corso

Flower Hub From Federated Apps to a Collaborative AI Ecosystem

Federated Training a 70B LLM

PharosAI Building the UK’s Largest Federated Multimodal Biomedical Dataset

The MLCommons and Flower Partnership

Accelerating the Adoption of FedAI

ISGlobal and Flower collaboration

SuperGrid Run Industry-grade Collaborative AI

FloraLab Removing Day-Zero Friction for FL on HPC

Fireside Chat with Alois Krtil

Federated Process Learning at the Edge

Position Federated Foundation Model Post-Training Should Focus on Open-Source

Flower Confidential Compute

Trust, Scale, and Connectivity in Federated AI Architecture

Welcome to Flower AI Summit 2026