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Found 7 papers, 1 papers with code
Privately Customizing Prefinetuning to Better Match User Data in Federated Learning
To this end, we propose FreD (Federated Private Fr\'echet Distance) -- a privately computed distance between a prefinetuning dataset and federated datasets.
FedSynth: Gradient Compression via Synthetic Data in Federated Learning
Model compression is important in federated learning (FL) with large models to reduce communication cost.
Papaya: Practical, Private, and Scalable Federated Learning
Our work tackles the aforementioned issues, sketches of some of the system design challenges and their solutions, and touches upon principles that emerged from building a production FL system for millions of clients.
Convex Latent Effect Logit Model via Sparse and Low-rank Decomposition
In this paper, we propose a convex formulation for learning logistic regression model (logit) with latent heterogeneous effect on sub-population.
Federated Learning with Buffered Asynchronous Aggregation
On the other hand, asynchronous aggregation of client updates in FL (i. e., asynchronous FL) alleviates the scalability issue.
Evaluating Lottery Tickets Under Distributional Shifts
The Lottery Ticket Hypothesis suggests large, over-parameterized neural networks consist of small, sparse subnetworks that can be trained in isolation to reach a similar (or better) test accuracy.
Efficient Online Hyperparameter Optimization for Kernel Ridge Regression with Applications to Traffic Time Series Prediction
Computational efficiency is an important consideration for deploying machine learning models for time series prediction in an online setting.
