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Federated Learning

Federated learning is originally created by Google. With the help of Federated Learning, machine learning models can learn on data sets located in different sites without having any training information shared between them.

Federated Learning Framework ‘Flower’ Has Released V.0.19 With A Lot of...

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This Article Is Based On The Research Article 'Flower 0.19 Release'. All Credit For This Research Goes To The Researchers of This Project đź‘Źđź‘Źđź‘Ź Please...

Microsoft AI Team Introduces “Federated Learning Utilities and Tools for Experimentation”...

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This Article Is Based On The Research Paper 'FLUTE: A SCALABLE, EXTENSIBLE FRAMEWORK FOR HIGH-PERFORMANCE FEDERATED LEARNING SIMULATIONS'. All Credit For This Research Goes...

Latest Paper From Amazon AI Research Analyzes And Explains The Challenges...

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This article summary is based on the research paper from Amazon: 'Federated learning challenges and opportunities: An outlook' All credits for this research goes to...

Researchers from MIT CSAIL Introduce ‘Privid’: an AI Tool, Build on...

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This research summary article is based on the paper 'Privid: Practical, Privacy-Preserving Video Analytics Queries' and MIT article 'Security tool guarantees privacy in surveillance...

Being Compatible With Any Programming Language And Machine Learning Framework; Flower...

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Flower is an end-to-end federated learning framework that allows for a smoother transition from simulation-based experimental research to system research on many real-world edge...

JAX + Flower For Federated Learning Gives Machine Learning Researchers The...

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Google researchers created JAX to conduct NumPy computations on GPUs and TPUs. DeepMind uses it to help and expedite its research, and it is...

Google AI Implements Machine Learning Model That Employs Federated Learning With...

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Bringing model training to the device extends beyond the usage of local models that make predictions on mobile devices. Federated Learning (FL) allows mobile...

Google’s Latest Machine Learning Research on Using Differential Privacy in Image...

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From recommendations to automatic picture classification, machine learning (ML) models are increasingly helpful for increased performance across several consumer products. Despite aggregating massive volumes...

Google Introduces ‘PipelineDP’: A New Differential Privacy Framework For Python Developers...

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Google unveiled a new milestone. a differential privacy framework, along with OpenMined that lets any Python developer handle data with differential privacy.  The two have been working on...

Introduction To Federated Learning: Enabling The Scaling Of Machine Learning Across...

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Large volumes of data are required for training machine learning models. The trained model is run on a cloud server that users can access...
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