Some key ways in which federated learning can improve machine learning models in healthcare include: Improved data diversity: Federated learning enables the use of data from multiple sources, ...

Another limit of federated machine learning is data labeling. Most machine learning models are supervised, which means they require training examples that are manually labeled by human annotators.

As machine learning becomes more pervasive in the data center and the cloud there will be a need to share and aggregate information and knowledge but without exposing or moving the underlying data.

Federated learning marks a milestone in enhancing collaborative model AI training. It is shifting the main approach to machine learning, moving away from the traditional centralized training ...

We are now collaborating with Elix to develop a federated learning package in addition to the technologies we have developed so far and release it as the drug discovery AI library ‘kMoL.’ ...

Federated Learning (FL) stands at the intersection of privacy preservation and decentralized data use, revolutionizing practical machine learning. This approach maintains data on local devices ...

The Recentive decision exemplifies the Federal Circuit’s skepticism toward claims that dress up longstanding business problems in machine-learning garb, while the USPTO’s examples confirm that ...

In response to the Anti-Money Laundering (AML) Act of 2020, the financial industry is exploring the use of artificial intelligence (AI) and machine learning (ML) for AML strategies, recognizing ...