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Clustered sampling: Low-variance and improved representativity for clients selection in federated learning
This work addresses the problem of optimizing communications between server and clients
in federated learning (FL). Current sampling approaches in FL are either biased, or non …
in federated learning (FL). Current sampling approaches in FL are either biased, or non …
GossipFL: A decentralized federated learning framework with sparsified and adaptive communication
Recently, federated learning (FL) techniques have enabled multiple users to train machine
learning models collaboratively without data sharing. However, existing FL algorithms suffer …
learning models collaboratively without data sharing. However, existing FL algorithms suffer …
Federated learning with compression: Unified analysis and sharp guarantees
In federated learning, communication cost is often a critical bottleneck to scale up distributed
optimization algorithms to collaboratively learn a model from millions of devices with …
optimization algorithms to collaboratively learn a model from millions of devices with …