Morpheo: Traceable Machine Learning on Hidden data
Morpheo is a transparent and secure machine learning platform collecting and analysing large datasets. It aims at building state-of-the art prediction models in various fields where data are sensitive. Indeed, it offers strong privacy of data and algorithm, by preventing anyone to read the data, apart from the owner and the chosen algorithms. Computations in Morpheo are orchestrated by a blockchain infrastructure, thus offering total traceability of operations. Morpheo aims at building an attractive economic ecosystem around data prediction by channelling crypto-money from prediction requests to useful data and algorithms providers. Morpheo is designed to handle multiple data sources in a transfer learning approach in order to mutualize knowledge acquired from large datasets for applications with smaller but similar datasets.
Code (0)
등록된 구현이 없습니다.
Tasks
BIG-bench Machine LearningPredictionTransfer LearningSimilar Papers 제목 키워드 기반
Latent Cognizance: What Machine Really Learns
Despite overwhelming achievements in recognition accuracy, extending an open-set capability -- ability to identify when the question is out of scope -- remains greatly challenging in a scalable machine learning inference…
Object RecognitionOpen Set LearningSign Language RecognitionSubstra: a framework for privacy-preserving, traceable and collaborative Machine Learning
Machine learning is promising, but it often needs to process vast amounts of sensitive data which raises concerns about privacy. In this white-paper, we introduce Substra, a distributed framework for privacy-preserving, …
BIG-bench Machine LearningFederated LearningPrivacy PreservingCrayotter: Traceable Multi-Agent Workflows for Long-Form Video Editing
Long-form video editing over heterogeneous footage requires agents to coordinate source selection, multimodal analysis, timeline construction, narration and subtitle alignment, rendering, and revision while exposing inte…
Traceable Black-box Watermarks for Federated Learning
Due to the distributed nature of Federated Learning (FL) systems, each local client has access to the global model, posing a critical risk of model leakage. Existing works have explored injecting watermarks into local mo…
Federated LearningSEQUENT: Towards Traceable Quantum Machine Learning using Sequential Quantum Enhanced Training
Applying new computing paradigms like quantum computing to the field of machine learning has recently gained attention. However, as high-dimensional real-world applications are not yet feasible to be solved using purely …
Hybrid Machine Learningimage-classificationImage ClassificationQuantum Machine Learning+1