OpenFGL: A Comprehensive Benchmark for Advancing Federated Graph Learning
Graph neural networks (GNNs) have emerged as powerful tools for capturing complex interactions in real-world entities and finding applications across ...
Tag: Federated
Asynchronous, dynamic, resource-efficient federated learning for a digital twin-powered IoT network
Digital twin (DT) technology is becoming increasingly popular as a method of providing Internet of Things (IoT) devices with dynamic ...
Federated learning with differential privacy for end-to-end speech recognition
*Equal taxpayers While federated learning (FL) has recently emerged as a promising approach to training machine learning models, it is ...
Sampleable anonymous aggregation for private federated data analysis
We return to the problem of designing scalable protocols for private statistics and private federated learning when each device has ...
Improved Modeling of Federated Data Sets Using Dirichlet Multinomial Mixtures
In practice, training using federated learning can be much slower than standard centralized training. This severely limits the amount of ...
pfl-research: Simulation framework to accelerate research in private federated learning
Federated learning (FL) is an emerging machine learning training paradigm in which clients own their data and collaborate to train ...
Enable data sharing through federated learning: A policy approach for chief digital officers
This is a guest blog post written by Nitin Kumar, a Lead Data Scientist at T and T Consulting Services, ...
Google AI proposes FAX: a JAX-based Python library to define scalable federated and distributed computations in the data center
In recent research, a team of Google Research researchers introduced FAX, an advanced software library built on top of JavaScript ...
Federated learning on AWS using FedML, Amazon EKS, and Amazon SageMaker
This post is co-written with Chaoyang He, Al Nevarez and Salman Avestimehr from FedML. Many organizations are implementing machine learning ...
Can differential privacy and federated learning protect your privacy? This article uncovers a major security flaw in machine learning systems
Federated learning has attracted increasing interest from the research community in recent years due to its ability to provide privacy-preserving ...
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