Federated Learning & Flower Community
Recorded talks of the Flower Summit 2021
The Flower Summit includes future directions in federated learning research such as:
Dr. Maria Börner - Adap GmbH
Recorded talk for "The Erium Podcast"
Die Nutzung von KI-Modellen nimmt weltweit zu. Oftmals sind die Daten auf mehreren Datenspeichern verteilt. Bei der herkömmlichen KI-Konfigurationen mit zentralem Training, wo die Daten in einer Cloud oder einem Server verarbeitet werden, gibt es jedoch mehrere Nachteile: Verletzung des Datenschutzes, hohe Latenzzeiten, komplexe Infrastruktur und hoher Energieverbrauch in Rechenzentren aufgrund der direkten Kühlung. Föderiertes Lernen bringt das KI-Training direkt zu den Datenspeichern und löst die genannten Probleme von vornherein... (Meetup Announcement)
Daniel J. Beutel - Adap GmbH
Recorded talk for Meetup-AI Global
Present AI applications are running mostly in data centers or the cloud with a centralized training approach. However, this requires a complex big data infrastructure resulting in limited availability and high latency. Regulatory constraints are an additional challenge that needs to be solved. Federated learning addresses these problems by moving AI training to the data being saved on edge-devices... (Meetup Announcement)
Machine learning is a tool that has typically been performed on large volumes of data in one place. As more computing happens at the edge on mobile and low power devices, the learning is being federated which brings a new set of challenges. Daniel Beutel co-created the Flower framework to make federated learning more manageable. In this episode he shares his motivations for starting the project, how you can use it for your own work, and the unique challenges and benefits that this emerging model offers...
Present AI applications are running mostly in data centers or cloud with a centralized training approach. However, this requires a complex big data infrastructure resulting in limited availability and high latency. Regulatory constraints are an additional challenge that needs to be solved. Federated learning addresses these problems by moving AI training to the data being saved on decentralized edge-devices... (Meetup Announcement)
Xinchi Qiu, Titouan Parcollet, Daniel J. Beutel, Taner Topal, Akhil Mathur, Nicholas D. Lane
NeurIPS 2020 Workshop: Tackling Climate Change with Machine Learning
Despite impressive results, deep learning-based technologies also raise severe privacy and environmental concerns induced by the training procedure often conducted in data centers. In response, alternatives to centralized training such as Federated Learning (FL) have emerged. Perhaps unexpectedly, FL in particular is starting to be deployed at a global scale by companies that must adhere to new legal demands and policies originating from governments and the civil society for privacy protection. However, the potential...
Daniel J. Beutel, Taner Topal, Akhil Mathur, Xinchi Qiu, Titouan Parcollet, Nicholas D. Lane
arXiv:2007.14390, Jul, 2020
Federated Learning (FL) has emerged as a promising technique for edge devices to collaboratively learn a shared prediction model, while keeping their training data on the device, thereby decoupling the ability to do machine learning from the need to store the data in the cloud. However, FL is difficult to implement and deploy in practice...
Mobile and embedded devices increasingly rely on deep neural networks to understand the world -- a formerly impossible feat that would have overwhelmed their system resources just a few years ago. The age of on-device artificial intelligence is upon us; but incredibly, these dramatic changes are just the beginning... (Meetup Announcement)
Flower is our open source federated learning framework. It offers a unified approach to federated learning. Federate any workload, any ML framework, and any programming language.