Federated Learning

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

AI ShortsSeptember 29, 2023

Was this response better or worse?BetterWorseSame It has been said that information theory and machine learning are "two sides of the same coin" because of their close relationship. One exquisite relationship is the fundamental similarity between probabilistic...

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

AI ShortsSeptember 29, 2023

Researchers have introduced a cutting-edge framework called MUTEX, short for "MUltimodal Task specification for robot EXecution," aimed at significantly advancing the capabilities of robots in assisting humans. The primary problem they tackle is the limitation of...

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

AI Paper SummaryFebruary 17, 2023

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

AI Paper SummaryDecember 13, 2022

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

AI ShortsDecember 11, 2022

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

AI Paper SummarySeptember 30, 2022

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...

AI Paper SummarySeptember 10, 2022

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

AI GovernanceSeptember 8, 2022

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

AI Paper SummarySeptember 4, 2022

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

AI Paper SummaryJune 26, 2022

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

AI Paper SummaryJune 17, 2022

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

AI EthicsJune 11, 2022

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

AI ShortsMay 28, 2022

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