Federated Learning

Large Language Models (LLMs) have emerged as a powerful ally for developers, promising to revolutionize how coding tasks are approached. By serving as intelligent assistants, LLMs have the potential to streamline various aspects of the development...
Developing middleware solutions for large language models (LLMs) represents an effort to bridge AI's theoretical capabilities and its practical applications in real-world scenarios. The challenge of navigating and processing enormous quantities of data within complex environments,...

University of Michigan Researchers Open-Source ‘FedScale’: a Federated Learning (FL) Benchmarking Suite with Realistic Datasets and a Scalable Runtime to Enable Reproducible FL Research...

Federated learning (FL) is a new machine learning (ML) environment in which a logically centralized coordinator orchestrates numerous dispersed clients (e.g., cellphones or laptops)...

Google AI and Tel Aviv Researchers Introduce FriendlyCore: A Machine Learning Framework For Computing Differentially Private Aggregations

Data analysis revolves around the central goal of aggregating metrics. The aggregation should be conducted in secret when the data points match personally identifiable...

In A New AI Research, Federated Learning Enables Big Data For Rare Cancer Boundary Detection

The number of primary observations produced by healthcare systems has dramatically increased due to recent technological developments and a shift in patient culture from...

IOM Releases Its Second Synthetic Dataset From Trafficking Victim Case Records Generated With Differential Privacy And AI From Microsoft

Researchers at Microsoft are committed to researching ways technology may help the world's most marginalized peoples improve their human rights situations. Their expertise spans...

Researchers Developed SmoothNets For Optimizing Convolutional Neural Network (CNN) Architecture Design For Differentially Private Deep Learning

Differential privacy (DP) is used in machine learning to preserve the confidentiality of the information that forms the dataset. The most used algorithm to...

Researchers Analyze the Current Findings on Confidential Computing-Assisted Machine Learning ML Security and Privacy Techniques Along with the Limitations in Existing Trusted Execution Environment...

The evolution of machine learning (ML) offers broader possibilities of use. However, wide applications also increase the risks of large attack surface on ML's...

3 Machine Learning Business Challenges Rooted in Data Sensitivity 

Machine Learning (ML) and, in particular, Deep Learning is drastically changing the way we conduct business as now data can be utilized to guide...

Researchers created a Novel Framework called ‘FedD3’ for Federated Learning in Resource-Constrained Edge Environments via Decentralized Dataset Distillation

For collaborative learning in large-scale distributed systems with a sizable number of networked clients, such as smartphones, connected cars, or edge devices, federated learning...

Researchers At Amazon Propose ‘AdaMix’, An Adaptive Differentially Private Algorithm For Training Deep Neural Network Classifiers Using Both Private And Public Image Data

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 Achieve Perfect Secrecy For Personal Tasks

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 (GNN) Framework For Both Effective And Privacy-Preserving Personalization

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 Fairness via Gradient Alignment

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

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