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Found 11 papers, 7 papers with code
Federated Reconstruction: Partially Local Federated Learning
We also describe the successful deployment of this approach at scale for federated collaborative filtering in a mobile keyboard application.
A Field Guide to Federated Optimization
Federated learning and analytics are a distributed approach for collaboratively learning models (or statistics) from decentralized data, motivated by and designed for privacy protection.
Motley: Benchmarking Heterogeneity and Personalization in Federated Learning
To better answer these questions, we propose Motley, a benchmark for personalized federated learning.
Learning a Compressed Sensing Measurement Matrix via Gradient Unrolling
Our experiments show that there is indeed additional structure beyond sparsity in the real datasets; our method is able to discover it and exploit it to create excellent reconstructions with fewer measurements (by a factor of 1. 1-3x) compared to the previous state-of-the-art methods.
Single Pass PCA of Matrix Products
In this paper we present a new algorithm for computing a low rank approximation of the product $A^TB$ by taking only a single pass of the two matrices $A$ and $B$.
Sparse Logistic Regression Learns All Discrete Pairwise Graphical Models
We show that this algorithm can recover any arbitrary discrete pairwise graphical model, and also characterize its sample complexity as a function of model width, alphabet size, edge parameter accuracy, and the number of variables.
Leveraging Sparsity for Efficient Submodular Data Summarization
The facility location problem is widely used for summarizing large datasets and has additional applications in sensor placement, image retrieval, and clustering.
Learning Distributions Generated by One-Layer ReLU Networks
We give a simple algorithm to estimate the parameters (i. e., the weight matrix and bias vector of the ReLU neural network) up to an error $\epsilon||W||_F$ using $\tilde{O}(1/\epsilon^2)$ samples and $\tilde{O}(d^2/\epsilon^2)$ time (log factors are ignored for simplicity).
Implicit Regularization and Convergence for Weight Normalization
For certain stepsizes of g and w , we show that they can converge close to the minimum norm solution.
Profit: Benchmarking Personalization and Robustness Trade-off in Federated Prompt Tuning
In many applications of federated learning (FL), clients desire models that are personalized using their local data, yet are also robust in the sense that they retain general global knowledge.
Prompt Public Large Language Models to Synthesize Data for Private On-device Applications
Pre-training on public data is an effective method to improve the performance for federated learning (FL) with differential privacy (DP).
