Flower
Summit 2023

What is Federated Learning? Getting Started with Flower + Common Beginner Questions

Welcome & Intro

Flower Research Update

Open Problems of Learning in Federated Settings

FedPM: Sparse Random Networks for Communication-Efficient Federated Learning

FedorAS: Federated Architecture Search under system heterogeneity

Federated Content-Based Medical Image Retrieval

Lessons learned from running a privacy tech challenge

Privacy-preserving federated analytics for financial crime detection

Secure federated learning applied to medical imaging with fully homomorphic encryption

Federated Self-Supervised Speech Technologies: Opportunities and Challenges

Enabling privacy-aware and carbon-aware machine learning, using Flower

Hamilton: A Novel Paradigm for Feature Engineering in Python (and how it is a natural companion to federated learning!)

Researchers Love These Tricks

After-Hours Reception

Federating iOS

Flower Hands-On Tutorial

Welcome & Intro

Flower Industry Update

Deploying Distributed Learning: Real-World Challenges and Opportunities from the Field

Dandelion: Privacy-Preserving Cross-Device FL in Brave Browser

Federated Feature Selection for Horizontal Federated Learning in IoT Networks

Federated Learning meets the shop floor in Siemens PCB production facilities

FLAME (Federated Learning for Anti-Money Laundering Enhancement) preventing International Financial Crime

Federated Learning with Weights & Biases

FedOps: Federated Learning Lifecycle Operations Management

Federated Learning Standards: A collaborative approach to improving research reproducibility and building an interoperable future

Nettle - Privacy-Preserving Federated Learning based on Flower and Carbyne Stack

Large-scale distributed deployments of federated learning using OpenStack

Designing a Privacy-Preserving Kubernetes Operator for Federated Learning

Exploration of Federated Learning in the United States Air Force with Flower

Industry Proof-of-Concept FL in 10 mins