Add to ORKGFederated Learning (FL) enables the training of Deep Learning models without centrally collecting possibly sensitive raw data. This paves the way for stronger privacy guarantees when building predictive models. The most used algorithms for FL are parameter-averaging based schemes (e.g., Federated Averaging) that, however, have well known limits: (i) Clients must implement the same model architecture; (ii) Transmitting model weights and model updates implies high communication cost, which scales up with the number of model parameters; (iii) In presence of non-IID data distributions, parameter-averaging aggregation schemes perform poorly due to client model drifts. Federated adaptations of regular Knowledge Distillation (KD) can solve and/or ...
Federated learning is a new scheme of distributed machine learning, which enables a large number of ...
Federated Learning (FL) enables distributed training by learners using local data, thereby enhancing...
Federated learning (FL) has been proposed as a privacy-preserving approach in distributed machine le...
Federated Learning (FL) is an emerging distributed learning paradigm under privacy constraint. Data ...
Is it possible to design an universal API for federated learning using which an ad-hoc group of data...
This paper presents FedX, an unsupervised federated learning framework. Our model learns unbiased re...
Federated learning (FL) is a privacy-preserving machine learning paradigm in which the server period...
Federated Learning is a machine learning paradigm where we aim to train machine learning models in a...
The uneven distribution of local data across different edge devices (clients) results in slow model ...
The advent of federated learning has facilitated large-scale data exchange amongst machine learning ...
Federated learning (FL) is an emerging machine learning paradigm involving multiple clients, e.g., m...
Federated learning (FL) is an important paradigm for training global models from decentralized data ...
Federated learning is a new scheme of distributed machine learning, which enables a large number of ...
Federated learning is a new scheme of distributed machine learning, which enables a large number of ...
Federated learning is a new scheme of distributed machine learning, which enables a large number of ...
Federated learning is a new scheme of distributed machine learning, which enables a large number of ...
Federated Learning (FL) enables distributed training by learners using local data, thereby enhancing...
Federated learning (FL) has been proposed as a privacy-preserving approach in distributed machine le...
Federated Learning (FL) is an emerging distributed learning paradigm under privacy constraint. Data ...
Is it possible to design an universal API for federated learning using which an ad-hoc group of data...
This paper presents FedX, an unsupervised federated learning framework. Our model learns unbiased re...
Federated learning (FL) is a privacy-preserving machine learning paradigm in which the server period...
Federated Learning is a machine learning paradigm where we aim to train machine learning models in a...
The uneven distribution of local data across different edge devices (clients) results in slow model ...
The advent of federated learning has facilitated large-scale data exchange amongst machine learning ...
Federated learning (FL) is an emerging machine learning paradigm involving multiple clients, e.g., m...
Federated learning (FL) is an important paradigm for training global models from decentralized data ...
Federated learning is a new scheme of distributed machine learning, which enables a large number of ...
Federated learning is a new scheme of distributed machine learning, which enables a large number of ...
Federated learning is a new scheme of distributed machine learning, which enables a large number of ...
Federated learning is a new scheme of distributed machine learning, which enables a large number of ...
Federated Learning (FL) enables distributed training by learners using local data, thereby enhancing...
Federated learning (FL) has been proposed as a privacy-preserving approach in distributed machine le...