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Found 11 papers, 3 papers with code
EM for Mixture of Linear Regression with Clustered Data
In many settings however, heterogeneous data may be generated in clusters with shared structures, as is the case in several applications such as federated learning where a common latent variable governs the distribution of all the samples generated by a client.
Variance-reduced Clipping for Non-convex Optimization
This is in clear contrast to the well-established assumption in folklore non-convex optimization, a. k. a.
Gradient Descent for Low-Rank Functions
When $r \ll p$, these complexities are smaller than the known complexities of $\mathcal{O}(p \log(1/\epsilon))$ and $\mathcal{O}(p/\epsilon^2)$ of {\gd} in the strongly convex and non-convex settings, respectively.
An Optimal Transport Approach to Personalized Federated Learning
In this paper, we focus on this problem and propose a novel personalized Federated Learning scheme based on Optimal Transport (FedOT) as a learning algorithm that learns the optimal transport maps for transferring data points to a common distribution as well as the prediction model under the applied transport map.
Straggler-Resilient Federated Learning: Leveraging the Interplay Between Statistical Accuracy and System Heterogeneity
Federated Learning is a novel paradigm that involves learning from data samples distributed across a large network of clients while the data remains local.
Robust Federated Learning: The Case of Affine Distribution Shifts
In such settings, the training data is often statistically heterogeneous and manifests various distribution shifts across users, which degrades the performance of the learnt model.
FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization
Federated learning is a distributed framework according to which a model is trained over a set of devices, while keeping data localized.
Robust and Communication-Efficient Collaborative Learning
We consider a decentralized learning problem, where a set of computing nodes aim at solving a non-convex optimization problem collaboratively.
CodedReduce: A Fast and Robust Framework for Gradient Aggregation in Distributed Learning
That is, it parallelizes the communications over a tree topology leading to efficient bandwidth utilization, and carefully designs a redundant data set allocation and coding strategy at the nodes to make the proposed gradient aggregation scheme robust to stragglers.
An Exact Quantized Decentralized Gradient Descent Algorithm
We consider the problem of decentralized consensus optimization, where the sum of $n$ smooth and strongly convex functions are minimized over $n$ distributed agents that form a connected network.
Coded Computation over Heterogeneous Clusters
There have been recent results that demonstrate the impact of coding for efficient utilization of computation and storage redundancy to alleviate the effect of stragglers and communication bottlenecks in homogeneous clusters.
Distributed, Parallel, and Cluster Computing Information Theory Information Theory
