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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.

Researchers At Amazon Propose ‘AdaMix’, An Adaptive Differentially Private Algorithm For...

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It is crucial to preserve privacy by restricting the amount of data that may be gathered about each training sample when training a deep...

Stanford AI Researchers Propose ‘FOCUS’: A Foundation Model Which Aims to...

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Machine learning holds the possibility of assisting people with personal activities. Personal jobs range from well-known activities like subject categorization over personal correspondence and...

Researchers From China Introduce ‘FedPerGNN’: A New Federated Graph Neural Network...

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This Article is written as a summay by Marktechpost Staff based on the paper 'A federated graph neural network framework for privacy-preserving personalization'. All...

Borealis AI Research Introduces fAux:  A New Approach To Test Individual...

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Machine learning models are trained on massive datasets with hundreds of thousands, if not billions, of parameters. However, how these models translate the input...

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 👏👏👏 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...