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Found 3 papers, 1 papers with code
Asynchronous Multi-Server Federated Learning for Geo-Distributed Clients
Federated learning (FL) systems enable multiple clients to train a machine learning model iteratively through synchronously exchanging the intermediate model weights with a single server.
Asynchronous Byzantine Federated Learning
Federated learning (FL) enables a set of geographically distributed clients to collectively train a model through a server.
Aergia: Leveraging Heterogeneity in Federated Learning Systems
Federated Learning (FL) is a popular approach for distributed deep learning that prevents the pooling of large amounts of data in a central server.
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