Add to ORKGDecentralized training of deep learning models enables on-device learning over networks, as well as efficient scaling to large compute clusters. Experiments in earlier works reveal that, even in a data-center setup, decentralized training often suffers from the degradation in the quality of the model: the training and test performance of models trained in a decentralized fashion is in general worse than that of models trained in a centralized fashion, and this performance drop is impacted by parameters such as network size, communication topology and data partitioning.We identify the changing consensus distance between devices as a key parameter to explain the gap between centralized and decentralized training. We show in theory that when t...
Deep neural networks are trained by solving huge optimization problems with large datasets and milli...
Decentralized machine learning is a promising emerging paradigm in view of global challenges of data...
Structure plays a key role in learning performance. In centralized computational systems, hyperparam...
Over the past decade, there has been a growing interest in large-scale and privacy-concerned machine...
The success of deep learning may be attributed in large part to remarkable growth in the size and co...
Decentralized training of deep learning models is a key element for enabling data privacy and on-dev...
Training a deep neural network (DNN) with a single machine consumes much time. To accelerate the tra...
Training a deep neural network (DNN) with a single machine consumes much time. To accelerate the tra...
Federated learning is a popular framework that enables harvesting edge resources’ computational powe...
Federated learning is a popular framework that enables harvesting edge resources’ computational powe...
Federated Learning is a well-known learning paradigm that allows the distributed training of machine...
Decentralized distributed learning is the key to enabling large-scale machine learning (training) on...
The distributed training of deep learning models faces two issues: efficiency and privacy. First of ...
Modern mobile devices have access to a wealth of data suitable for learning models, which in turn ca...
As deep learning techniques become more and more popular, there is the need to move these applicatio...
Deep neural networks are trained by solving huge optimization problems with large datasets and milli...
Decentralized machine learning is a promising emerging paradigm in view of global challenges of data...
Structure plays a key role in learning performance. In centralized computational systems, hyperparam...
Over the past decade, there has been a growing interest in large-scale and privacy-concerned machine...
The success of deep learning may be attributed in large part to remarkable growth in the size and co...
Decentralized training of deep learning models is a key element for enabling data privacy and on-dev...
Training a deep neural network (DNN) with a single machine consumes much time. To accelerate the tra...
Training a deep neural network (DNN) with a single machine consumes much time. To accelerate the tra...
Federated learning is a popular framework that enables harvesting edge resources’ computational powe...
Federated learning is a popular framework that enables harvesting edge resources’ computational powe...
Federated Learning is a well-known learning paradigm that allows the distributed training of machine...
Decentralized distributed learning is the key to enabling large-scale machine learning (training) on...
The distributed training of deep learning models faces two issues: efficiency and privacy. First of ...
Modern mobile devices have access to a wealth of data suitable for learning models, which in turn ca...
As deep learning techniques become more and more popular, there is the need to move these applicatio...
Deep neural networks are trained by solving huge optimization problems with large datasets and milli...
Decentralized machine learning is a promising emerging paradigm in view of global challenges of data...
Structure plays a key role in learning performance. In centralized computational systems, hyperparam...