Clustered sampling: Low-variance and improved representativity for clients selection in federated learning

Y Fraboni, R Vidal, L Kameni… - … on Machine Learning, 2021 - proceedings.mlr.press
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 …

GossipFL: A decentralized federated learning framework with sparsified and adaptive communication

Z Tang, S Shi, B Li, X Chu - IEEE Transactions on Parallel and …, 2022 - ieeexplore.ieee.org
Recently, federated learning (FL) techniques have enabled multiple users to train machine
learning models collaboratively without data sharing. However, existing FL algorithms suffer …

Federated learning with compression: Unified analysis and sharp guarantees

F Haddadpour, MM Kamani… - International …, 2021 - proceedings.mlr.press
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 …