Research day (30 May 2023)
What is Federated Learning? Getting Started with Flower + Common Beginner Questions
Tutorial
Charles Beauville
Welcome & Intro
Nicholas Lane
Flower Research Update
Daniel J. Beutel
Open Problems of Learning in Federated Settings
Tian Li
FedPM: Sparse Random Networks for Communication-Efficient Federated Learning
Francesco Pase
FedorAS: Federated Architecture Search under system heterogeneity
Stefanos Laskaridis
Federated Content-Based Medical Image Retrieval
Yuandou Wang
Zahra Tabatabaei
Lessons learned from running a privacy tech challenge
Dave Buckley
Privacy-preserving federated analytics for financial crime detection
Sasi Murakonda
Secure federated learning applied to medical imaging with fully homomorphic encryption
Xavier Lessage
Leandro Collier
Federated Self-Supervised Speech Technologies: Opportunities and Challenges
Titouan Parcollet
Enabling privacy-aware and carbon-aware machine learning, using Flower
Dennis Grinwald
Hamilton: A Novel Paradigm for Feature Engineering in Python (and how it is a natural companion to federated learning!)
Elijah Ben Izzy
Researchers Love These Tricks
Javier Fernandez
After-Hours Reception
Flower Labs
Industry day (31 May 2023)
Federating iOS
Tutorial, In-Person only. Seminar Room FW26.
Daniel Nata Nugraha
Flower Hands-On Tutorial
Tutorial, In-Person only. Seminar Room FW26
Adam Narożniak
Flower Industry Update
Deploying Distributed Learning: Real-World Challenges and Opportunities from the Field
Katharine Jarmul
Dandelion: Privacy-Preserving Cross-Device FL in Brave Browser
Lorenzo Minto
Federated Feature Selection for Horizontal Federated Learning in IoT Networks
Xunzheng Zhang
Federated Learning meets the shop floor in Siemens PCB production facilities
Anne Mareike Schlinkert
Leonhard Kunczik
FLAME (Federated Learning for Anti-Money Laundering Enhancement) preventing International Financial Crime
Robert Norvill
Federated Learning with Weights & Biases
Ryan McConville
FedOps: Federated Learning Lifecycle Operations Management
SeMo Yang
KangYoon Lee
Federated Learning Standards: A collaborative approach to improving research reproducibility and building an interoperable future
Patrick Foley
Nettle - Privacy-Preserving Federated Learning based on Flower and Carbyne Stack
Dr. Sven Trieflinger
Large-scale distributed deployments of federated learning using OpenStack
Michał Daniłowski
Designing a Privacy-Preserving Kubernetes Operator for Federated Learning
Juan Marcelo Parra Ullauri
Exploration of Federated Learning in the United States Air Force with Flower
Chancellor Johnstone
Industry Proof-of-Concept FL in 10 mins
Tutorial, Hybrid