Federated Learning Archives

Microsoft AI Team Introduces “Federated Learning Utilities and Tools for Experimentation” (FLUTE): A High-Performance Open-Source Platform For Federated Learning Research And Offline Simulations

AI Paper SummaryMay 17, 2022

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 And Developments in The Field Of Federated Learning

AI Paper SummaryApril 17, 2022

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 Differential Privacy, to Guarantee Privacy in Video Footage from Surveillance Cameras

AI Paper SummaryMarch 30, 2022

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 Team Releases Flower 0.18 With Cool New Updates For Federated Learning

AI Paper SummaryMarch 29, 2022

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 Flexibility To Use The Deep Learning Framework For Their Projects

AI ShortsMarch 28, 2022

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 Differential Privacy Guarantees

AI Paper SummaryMarch 10, 2022

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 Classification on a Large Scale

AI Paper SummaryFebruary 11, 2022

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 To Process Data

AI ShortsFebruary 9, 2022

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 Decentralized Data Whilst Preserving Data Privacy

AI ShortsJanuary 25, 2022

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

Hierarchical Federated Learning-Based Anomaly Detection Using Digital Twins For Internet of Medical Things (IoMT)

AI Paper SummaryJanuary 1, 2022

Smart healthcare services can be provided by using Internet of Things (IoT) technologies that monitor the health conditions of patients and their vital body...

Google AI Introduces ‘Federated Reconstruction’ Framework That Enables Scalable Partially Local Federated Learning

AI Paper SummaryDecember 28, 2021

Federated learning is a machine learning technique in which an algorithm is trained across numerous decentralized edge devices or servers, keeping local data samples...

Researchers Propose ‘ProxyFL’: A Novel Decentralized Federated Learning Scheme For Multi-Institutional Collaborations Without Sacrificing Data Privacy

AI Paper SummaryDecember 12, 2021

Tight rules generally govern data sharing in highly regulated industries like finance and healthcare. Federated learning is a distributed learning system that allows multi-institutional...

Federated Learning

Recent articles

How Large Language Models are Redefining Data Compression and Providing Unique Insights into Machine Learning Scalability? Researchers from DeepMind Introduce a Novel Compression Paradigm

Researchers from UT Austin Introduce MUTEX: A Leap Towards Multimodal Robot Instruction with Cross-Modal Reasoning

Researchers from the University of Washington and Google have Developed Distilling Step-by-Step Technology to Train a Dedicated Small Machine Learning Model with Less Data

This AI Paper Proposes LLM-Grounder: A Zero-Shot, Open-Vocabulary Approach to 3D Visual Grounding for Next-Gen Household Robots

This AI Paper Dives into Embodied Evaluations: Unveiling the Tong Test as a Novel Benchmark for Progress Toward Artificial General Intelligence