Add to ORKGFederated learning algorithms are gaining increasing interest, and their effective exploitation asks for a flexible yet efficent support to the required distributed computations. Moreover, the availability of off-the-shelf FL implementations of main Data Mining algorithms is crucial for the success of the supporting platform. The thesis work proceeded along these two development directions: improvement of the required middleware support, and coding of FL adaptations of Data Mining algorithms to be directly used on top of the middleware
Federated learning (FL) is a type of distributed machine learning approacs that trains global models...
In the last decade, research in AI (artificial intelligence) related technologies have been evolving...
Federated learning allows multiple parties to collaboratively develop a deep learning model, without...
Federated learning algorithms are gaining increasing interest, and their effective exploitation asks...
Arguably the biggest strength of the functional programming language Erlang is how straightforward i...
A modern connected car produces gigabytes to terabytes of data per day. Collecting data generated by...
Federated learning (FL) is a machine learning setting where many clients (e.g., mobile devices or wh...
Federated Learning now a days has emerged as a promising standard for machine learning model trainin...
The federated learning technique (FL) supports the collaborative training of machine learning and de...
Federated Learning (FL) is a technique to train machine learning (ML) models on decentralized data, ...
Federated learning (FL) is a decentralized machine learning (ML) method that enables model training ...
Machine Learning (ML) enables the creation of a new generation of applications that 'learn' from col...
International audienceIoT devices produce ever growing amounts of data. Traditional cloud-based appr...
Federated learning allows the training of a model from the distributed data of many clients under th...
The communication and networking field is hungry for machine learning decision-making solutions to r...
Federated learning (FL) is a type of distributed machine learning approacs that trains global models...
In the last decade, research in AI (artificial intelligence) related technologies have been evolving...
Federated learning allows multiple parties to collaboratively develop a deep learning model, without...
Federated learning algorithms are gaining increasing interest, and their effective exploitation asks...
Arguably the biggest strength of the functional programming language Erlang is how straightforward i...
A modern connected car produces gigabytes to terabytes of data per day. Collecting data generated by...
Federated learning (FL) is a machine learning setting where many clients (e.g., mobile devices or wh...
Federated Learning now a days has emerged as a promising standard for machine learning model trainin...
The federated learning technique (FL) supports the collaborative training of machine learning and de...
Federated Learning (FL) is a technique to train machine learning (ML) models on decentralized data, ...
Federated learning (FL) is a decentralized machine learning (ML) method that enables model training ...
Machine Learning (ML) enables the creation of a new generation of applications that 'learn' from col...
International audienceIoT devices produce ever growing amounts of data. Traditional cloud-based appr...
Federated learning allows the training of a model from the distributed data of many clients under th...
The communication and networking field is hungry for machine learning decision-making solutions to r...
Federated learning (FL) is a type of distributed machine learning approacs that trains global models...
In the last decade, research in AI (artificial intelligence) related technologies have been evolving...
Federated learning allows multiple parties to collaboratively develop a deep learning model, without...