Add to ORKGMachine learning algorithms are undoubtedly one of the most popular algorithms in recent years, and neural networks have demonstrated unprecedented precision. In daily life, different communities may have different user characteristics, which also means that training a strong model requires the union of different communities, so the privacy issue needs to be solved urgently. Federated learning is a popular privacy solution, each community does not need to expose specific data, but only needs to upload sub-models to the coordination server to train more powerful models. However, federated learning also has some problems, such as the security and fairness of the coordination server. A proven solution to the problem is a decentralized implemen...
Establishing how a set of learners can provide privacy-preserving federated learning in a fully dece...
Federated Learning starts to give a new perspective regarding the applicability of machine learning ...
A common privacy issue in traditional machine learning is that data needs to be disclosed for the tr...
Federated learning (FL) is a promising framework for distributed machine learning that trains models...
The advent of machine learning techniques has given rise to modern devices with built-in models for ...
Federated learning (FL) is a promising decentralized deep learning technology, which allows users to...
The growth of information technology has resulted in a massive escalation of data and the demand for...
Federated learning (FL) is a promising technical support to the vision of ubiquitous artificial inte...
Reliable and timely traffic patterns have become an increasingly critical aspect of intelligent tran...
Federated learning is an improved version of distributed machine learning that further offloads oper...
Federated learning enables multiple users to collaboratively train a global model using the users’ p...
Privacy in today's world is a very important topic and all the more important when sizeable amounts ...
Driven by privacy concerns and the visions of deep learning, the last four years have witnessed a pa...
International audienceFederated learning (FL) is a distributed machine learning (ML) technique that ...
Federated learning suffers from several privacy-related issues that expose the participants to vario...
Establishing how a set of learners can provide privacy-preserving federated learning in a fully dece...
Federated Learning starts to give a new perspective regarding the applicability of machine learning ...
A common privacy issue in traditional machine learning is that data needs to be disclosed for the tr...
Federated learning (FL) is a promising framework for distributed machine learning that trains models...
The advent of machine learning techniques has given rise to modern devices with built-in models for ...
Federated learning (FL) is a promising decentralized deep learning technology, which allows users to...
The growth of information technology has resulted in a massive escalation of data and the demand for...
Federated learning (FL) is a promising technical support to the vision of ubiquitous artificial inte...
Reliable and timely traffic patterns have become an increasingly critical aspect of intelligent tran...
Federated learning is an improved version of distributed machine learning that further offloads oper...
Federated learning enables multiple users to collaboratively train a global model using the users’ p...
Privacy in today's world is a very important topic and all the more important when sizeable amounts ...
Driven by privacy concerns and the visions of deep learning, the last four years have witnessed a pa...
International audienceFederated learning (FL) is a distributed machine learning (ML) technique that ...
Federated learning suffers from several privacy-related issues that expose the participants to vario...
Establishing how a set of learners can provide privacy-preserving federated learning in a fully dece...
Federated Learning starts to give a new perspective regarding the applicability of machine learning ...
A common privacy issue in traditional machine learning is that data needs to be disclosed for the tr...