Explore federated machine learning on the BEAM in this conference talk from Code BEAM Europe 2022. Dive into the challenges of data bottlenecks in machine learning and discover how federated learning can address these issues. Learn about the motivation behind this approach, its implementation on the BEAM, and specific algorithms used. Gain insights into future work in this field and participate in a Q&A session. Perfect for developers interested in machine learning, distributed systems, and Erlang/BEAM technologies.
Smashing the Data Bottleneck with Federated Machine Learning on the BEAM
Overview
Syllabus
00:00 - - Agenda
01:48 - - Machine Learning
04:14 - - Data Bottleneck
07:10 - - Federated Machine Learning
09:57 - - Motivation
11:59 - - Challenges
13:15 - - Federated Machine Learning on the BEAM
21:38 - - Algorithms
26:49 - - Future Work
30:32 - - QnA
Taught by
Code Sync
Related Courses
-
A Serverless Runtime on the BEAM - Code BEAM Europe 2022
-
Federated Learning - Machine Learning on Decentralized Data
5.0 -
SHERPA: Explainable Robust Algorithms for Privacy-Preserved Federated Learning in Future Networks
-
Introduction to Federated Learning with Flower - A Hands-on Tutorial
-
What, Why and How of Federated Machine Learning – Implementation Using FATE, KubeFATE, and FATE-Operator for Kubeflow
-
LOKI: Large-Scale Data Reconstruction Attack Against Federated Learning Through Model Manipulation - 2024
