Federated Learning

NVIDIA has recently introduced NV-Embed on Hugging Face, a revolutionary embedding model poised to redefine the landscape of NLP. This model, characterized by its impressive versatility and performance, has taken the top spot across multiple tasks...
Many developers and researchers working with large language models face the challenge of fine-tuning the models efficiently and effectively. Fine-tuning is essential for adapting a model to specific tasks or improving its performance, but it often...

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 ItsĀ Second Synthetic DatasetĀ 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 SensitivityĀ 

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: Ā 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...

Snowflake AI Research Team Unveils Arctic: An Open-Source Enterprise-Grade Large Language Model (LLM) with...

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Snowflake AI Research has launched the Arctic, a cutting-edge open-source large language model (LLM) specifically designed for enterprise AI applications, setting a new standard...

Google DeepMind Releases RecurrentGemma: One of the Strongest 2B-Parameter Open Language Models Designed for...

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Language models are the backbone of modern artificial intelligence systems, enabling machines to understand and generate human-like text. These models, which process and predict...

Finally, the Wait is Over: Meta Unveils Llama 3, Pioneering a New Era in...

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Meta has revealed its latest large language model, the Meta Llama 3, which is a major breakthrough in the field of AI. This new model is not just...

TrueFoundry Releases Cognita: An Open-Source RAG Framework for Building Modular and Production-Ready Applications

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The field of artificial intelligence is rapidly evolving, andĀ takingĀ a prototype to production stage can be quite challenging. However, TrueFoundry has recently introduced a new...

Meet Zamba-7B: Zyphra’s Novel AI Model That’s Small in Size and Big on Performance

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In the race to create more efficient and powerful AI models, Zyphra has unveiled a significant breakthrough with its new Zamba-7B model. This compact,...

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