March 14-15 / London
In person & virtual
Flower AI
Summit 2024
The world's largest Federated Learning conference
A big thank you to all who joined us, both online and in person, and a heartfelt appreciation to our wonderful speakers for being part of the Flower AI Summit 2024. If you missed any of the sessions, there is no need to worry! You will be able to access the talks shortly. In the meantime make sure to join our Slack Community!
AI Research Day
March 14
- Flower Ecosystem Roadmap for 2024
- Advances in Decentralized Learning
- Training LLMs using Sensitive Data
- Accelerating Basic Science with FL
- Scalable FLOps Tools
- Large-scale Research Deployments using FL
- Dozens of FL Research Results built on Flower
Daniel J. Beutel
Co-founder & CEO
Flower Research Update
Virginia Smith
Assistant Professor
On Privacy and Personalization in Federated Learning
Minhaj Alam
Assistant Professor
FL Diagnosis of Macular Degeneration
Adam Narożniak
Data Scientist
Flower Datasets
Hui Guan
Assistant Professor
Personalization and Concept Drift in FL
Bing Luo
Assistant Professor
FedCampus: A Privacy-preserving Smart Campus
Javier Fernandez & Yan Gao
Research Scientists
Flower Community Initiatives: From LLMs to SoR
KangYoon Lee
Professor
Enhancing Federated Learning with FedOps for the FL Marketplace
Andrew Soltan
Fellow in Clinical Artificial Intelligence
Scalable and low-cost Federated Learning in the NHS: Flower and micro-computing
Judith Sáinz-Pardo
Data Science Researcher
Federated AI in the European Open Science Cloud
Nic Lane
Co-founder & CSO
Flower Surprise Annoucement
Borja Balle
Researcher in Machine Learning and Differential Privacy
Towards practical differentially private training
Poster and Demo Presentations
AI Research Day
| Presenters | Affiliation | Title | |
|---|---|---|---|
 Data Science PhD Student | Sapienza University of Rome | FedArtML recharged with feature + quantity skew partition and non-IID quantification methods | |
 Full Professor |  Assistant Professor | University of Messina | FedROS: The ROS Framework for Federated Learning on Mobile Edge Devices |
 Visiting Stats and ML Researcher | Università della Svizzera Italiana | Hierarchical Bayes Approaches for Federated Learning | |
 PhD Candidate - Health Data Science | Public Health Resident MD | State of the Art of Health Federated Learning: Lessons from a Systematic Review | |
 Research Associate | Technische Universität Berlin | Exploring the interplay between FL and energy systems | |
 Speech & NLP Researcher | Queen Mary University of London | Federated Learning for Collaborative Content Moderation in the Fediverse | |
 Researcher |  Researcher | Gachon University | Supporting FedOps for Cross Silo Scenarios |
 Software Engineer |  Assistant Professor/ Senior Lecturer | Anglia Ruskin University | Confidential Heartbeat: Harmonizing Diverse Dataset for Cardiovascular Prognosis with Vertical Federated Learning |
 Postgraduate Student | Cambridge ML Systems Lab | Breaking Physical and Linguistic Borders: Multilingual Federated Prompt Tuning for Low-Resource Languages | |
 Artificial Intelligence Engineer | HI Iberia | GREEN [Collaborative intelligence for sustainable cities] | |
 Research Software Engineer |  Student and Software Developer | KAUST & University of Ljubljana | CoLExT: collaborative learning experimentation testbed |
 Data Scientist | ei3 | Inverse-PID: A Mathematical Approach towards Detecting Real-World Wear & Tear | |
 Research Associate & Developer | University of Portsmouth | Federated Learning based Robust Android Malware Detection: Label-Flipping Attacks and Defenses | |
 Data Scientist | GENXT | Federated learning for data collaboration in genomics | |
 Lead AI Research Engineer | NEWTWEN | Adaptive Compression in Federated Learning via Side Information | |
Assistant Professor |  Systems programmer | Duke Kunshan University | FedCampus: a Privacy-preserving Data Platform for Smart Campus with Federated Learning and Analytics |
 PhD student in Machine Learning | Technische Universität Berlin & BIFOLD | Subspace Training for Federated Learning |
AI Industry Day
March 15
- Industry Use-cases built on Flower by: Amazon, Zenseact, US Airforce
- FL Standards and Interoperability: The Flower, Intel and OpenFL partnership
- NHS Experimental Deployment of Flower for 130,000 Patients
- Survey of Early Commercial Adopters of Privacy-enhancing ML
- New Federated Dataset and Benchmark for Self-driving Cars
- Achieving Regulatory Compliance with Federated Approaches
- Practical Homomorphic Encryption for FL by combining Flower and Zama
Daniel J. Beutel
Co-founder & CEO
Flower Industry Update
Tim Hospedales
Head of Samsung AI
Overlooked desiderata for real-world FL
Sherry Ding
Senior AI/ ML Solutions Architect
Flower on Amazon SageMaker
Nathan Gaw
Assistant Professor of Data Science
US Military Applications using Federated Learning
Charles Kerrigan & Bill Marino
Lawyer/ PhD Student - Machine Learning
FL under AI Regulation
David Emerson
Applied Machine Learning Scientist
FL4Health: Private and Personal Clinical Modelling
Roman Bredehoft
Machine Learning Engineer
Private Inference with Fully Homomorphic Encryption (FHE) and FL
Mohammad Naseri & Pan Heng
Research Scientists
Flower support for Differential Privacy and Secure Aggregation
Mina Alibeigi
AI Researcher
Federated AI for vehicles
Calum Inverarity
Senior Researcher
The PETs Landscape
Nic Lane
Co-founder & CSO
Flower Pilot Program: Batch Two
Valerio Maggio
Data Scientist Advocate
Portable and reproducible deep learning environments with Flower and Conda
Poster and Demo Presentations
AI Industry Day
| Presenters | Affiliation | Title | |
|---|---|---|---|
Data Science PhD Student | Sapienza University of Rome | FedArtML recharged with feature + quantity skew partition and non-IID quantification methods | |
Full Professor | Assistant Professor | University of Messina | Homomorphic Encryption for Federated Learning: A Comparison Study on Flower |
Visiting Stats and ML Researcher | Università della Svizzera Italiana | Hierarchical Bayes Approaches for Federated Learning | |
PhD Candidate - Health Data Science | Public Health Resident MD | State of the Art of Health Federated Learning: Lessons from a Systematic Review | |
Research Associate | Technische Universität Berlin | Exploring the interplay between FL and energy systems | |
Speech & NLP Researcher | Queen Mary University of London | Federated Learning for Collaborative Content Moderation in the Fediverse | |
Researcher | Researcher | Gachon University | Supporting FedOps for Mobile Device Scenarios |
Software Engineer | Assistant Professor/ Senior Lecturer | Anglia Ruskin University | Confidential Heartbeat: Harmonizing Diverse Dataset for Cardiovascular Prognosis with Vertical Federated Learning |
Postgraduate Student | Cambridge ML Systems Lab | Enhancing Data Quality in Federated Fine-Tuning of Foundation Models | |
Artificial Intelligence Engineer | HI Iberia | GREEN [Collaborative intelligence for sustainable cities] | |
Research Software Engineer | Student and Software Developer | KAUST & University of Ljubljana | CoLExT: collaborative learning experimentation testbed |
Data Scientist | ei3 | Inverse-PID: A Mathematical Approach towards Detecting Real-World Wear & Tear | |
Research Associate & Developer | University of Portsmouth | Federated Learning based Robust Android Malware Detection: Label-Flipping Attacks and Defenses | |
Data Scientist | GENXT | Federated learning for data collaboration in genomics | |
Lead AI Research Engineer | NEWTWEN | Adaptive Compression in Federated Learning via Side Information | |
Assistant Professor | Systems programmer | Duke Kunshan University | FedCampus: a Privacy-preserving Data Platform for Smart Campus with Federated Learning and Analytics |
PhD student in Machine Learning | Technische Universität Berlin & BIFOLD | Subspace Training for Federated Learning |