This project will explore Federated Learning (FL) and its potential to address data privacy issues. It will investigate innovative FL architecture and protocols to…
Browsing: Federated Learning
FedML recently closed $11.5 million in seed funding to expand development and adoption for its distributed MLOps platform, which helps companies efficiently train and…
This paper proposes a two-layer accumulated quantized compression algorithm (TLAQC) to reduce the communication cost of federated learning. TLAQC introduces a revised quantization method…
OctaiPipe, the UK’s leading Federated Learning Platform for IoT, has won Trustworthy AI funding from UKRI as part of its BridgeAI programme. The team…
MLEDGE (Cloud and Edge Machine Learning) is a project that seeks to implement Federated Learning (FL) as an independent but optimized cross-sector layer on…
Federated learning is a distributed learning framework that enables multi-institutional collaborations on decentralized data with improved protection for each collaborator’s data privacy. This paper…
Artificial intelligence (AI) has made significant progress over the years, and it is now being used in various industries. AI app development is a…
as explainable AI (XAI) is gaining traction as a way to make AI models more transparent and understandable. XAI is a set of techniques…
This article presents a flexible federated learning scheme that allows machine learning researchers to tap into heterogeneously labeled data from multiple institutions and utilize…
FedML has announced $6 million in funding to spearhead a collaborative AI movement that enables companies and developers to work together on machine learning…