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Found 6 papers, 2 papers with code
FedFisher: Leveraging Fisher Information for One-Shot Federated Learning
Standard federated learning (FL) algorithms typically require multiple rounds of communication between the server and the clients, which has several drawbacks, including requiring constant network connectivity, repeated investment of computational resources, and susceptibility to privacy attacks.
FedExP: Speeding Up Federated Averaging via Extrapolation
Federated Averaging (FedAvg) remains the most popular algorithm for Federated Learning (FL) optimization due to its simple implementation, stateless nature, and privacy guarantees combined with secure aggregation.
FedVARP: Tackling the Variance Due to Partial Client Participation in Federated Learning
To remedy this, we propose FedVARP, a novel variance reduction algorithm applied at the server that eliminates error due to partial client participation.
Maximizing Global Model Appeal in Federated Learning
We provide convergence guarantees for MaxFL and show that MaxFL achieves a $22$-$40\%$ and $18$-$50\%$ test accuracy improvement for the training clients and unseen clients respectively, compared to a wide range of FL modeling approaches, including those that tackle data heterogeneity, aim to incentivize clients, and learn personalized or fair models.
Leveraging Spatial and Temporal Correlations in Sparsified Mean Estimation
We study the problem of estimating at a central server the mean of a set of vectors distributed across several nodes (one vector per node).
Adaptive Quantization of Model Updates for Communication-Efficient Federated Learning
Communication of model updates between client nodes and the central aggregating server is a major bottleneck in federated learning, especially in bandwidth-limited settings and high-dimensional models.
