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Found 50 papers, 21 papers with code
Federated Optimization with Doubly Regularized Drift Correction
Federated learning is a distributed optimization paradigm that allows training machine learning models across decentralized devices while keeping the data localized.
Non-Convex Stochastic Composite Optimization with Polyak Momentum
In this paper, we focus on the stochastic proximal gradient method with Polyak momentum.
Locally Adaptive Federated Learning via Stochastic Polyak Stepsizes
We prove that FedSPS converges linearly in strongly convex and sublinearly in convex settings when the interpolation condition (overparametrization) is satisfied, and converges to a neighborhood of the solution in the general case.
Synthetic data shuffling accelerates the convergence of federated learning under data heterogeneity
In this paper, we establish a precise and quantifiable correspondence between data heterogeneity and parameters in the convergence rate when a fraction of data is shuffled across clients.
On Convergence of Incremental Gradient for Non-Convex Smooth Functions
In machine learning and neural network optimization, algorithms like incremental gradient, and shuffle SGD are popular due to minimizing the number of cache misses and good practical convergence behavior.
Revisiting Gradient Clipping: Stochastic bias and tight convergence guarantees
In particular, we show that (i) for deterministic gradient descent, the clipping threshold only affects the higher-order terms of convergence, (ii) in the stochastic setting convergence to the true optimum cannot be guaranteed under the standard noise assumption, even under arbitrary small step-sizes.
Decentralized Gradient Tracking with Local Steps
Gradient tracking (GT) is an algorithm designed for solving decentralized optimization problems over a network (such as training a machine learning model).
On the effectiveness of partial variance reduction in federated learning with heterogeneous data
In this paper, we first revisit the widely used FedAvg algorithm in a deep neural network to understand how data heterogeneity influences the gradient updates across the neural network layers.
Sharper Convergence Guarantees for Asynchronous SGD for Distributed and Federated Learning
In this work (i) we obtain a tighter convergence rate of $\mathcal{O}\!\left(\sigma^2\epsilon^{-2}+ \sqrt{\tau_{\max}\tau_{avg}}\epsilon^{-1}\right)$ without any change in the algorithm where $\tau_{avg}$ is the average delay, which can be significantly smaller than $\tau_{\max}$.
Data-heterogeneity-aware Mixing for Decentralized Learning
Decentralized learning provides an effective framework to train machine learning models with data distributed over arbitrary communication graphs.
Tackling benign nonconvexity with smoothing and stochastic gradients
Non-convex optimization problems are ubiquitous in machine learning, especially in Deep Learning.
An Improved Analysis of Gradient Tracking for Decentralized Machine Learning
We consider decentralized machine learning over a network where the training data is distributed across $n$ agents, each of which can compute stochastic model updates on their local data.
The Peril of Popular Deep Learning Uncertainty Estimation Methods
Secondly, we show on a 2D toy example that both BNNs and MCDropout do not give high uncertainty estimates on OOD samples.
Breaking the centralized barrier for cross-device federated learning
Federated learning (FL) is a challenging setting for optimization due to the heterogeneity of the data across different clients which gives rise to the client drift phenomenon.
Linear Speedup in Personalized Collaborative Learning
Collaborative training can improve the accuracy of a model for a user by trading off the model's bias (introduced by using data from other users who are potentially different) against its variance (due to the limited amount of data on any single user).
ProgFed: Effective, Communication, and Computation Efficient Federated Learning by Progressive Training
Federated learning is a powerful distributed learning scheme that allows numerous edge devices to collaboratively train a model without sharing their data.
RelaySum for Decentralized Deep Learning on Heterogeneous Data
A key challenge, primarily in decentralized deep learning, remains the handling of differences between the workers' local data distributions.
On Second-order Optimization Methods for Federated Learning
We consider federated learning (FL), where the training data is distributed across a large number of clients.
Semantic Perturbations with Normalizing Flows for Improved Generalization
We find that our latent adversarial perturbations adaptive to the classifier throughout its training are most effective, yielding the first test accuracy improvement results on real-world datasets -- CIFAR-10/100 -- via latent-space perturbations.
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.
Masked Training of Neural Networks with Partial Gradients
State-of-the-art training algorithms for deep learning models are based on stochastic gradient descent (SGD).
Critical Parameters for Scalable Distributed Learning with Large Batches and Asynchronous Updates
It has been experimentally observed that the efficiency of distributed training with stochastic gradient (SGD) depends decisively on the batch size and -- in asynchronous implementations -- on the gradient staleness.
Quasi-Global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data
In this paper, we investigate and identify the limitation of several decentralized optimization algorithms for different degrees of data heterogeneity.
Consensus Control for Decentralized Deep Learning
Decentralized training of deep learning models enables on-device learning over networks, as well as efficient scaling to large compute clusters.
A Linearly Convergent Algorithm for Decentralized Optimization: Sending Less Bits for Free!
Decentralized optimization methods enable on-device training of machine learning models without a central coordinator.
On Communication Compression for Distributed Optimization on Heterogeneous Data
Lossy gradient compression, with either unbiased or biased compressors, has become a key tool to avoid the communication bottleneck in centrally coordinated distributed training of machine learning models.
Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning
Federated learning (FL) is a challenging setting for optimization due to the heterogeneity of the data across different clients which gives rise to the client drift phenomenon.
On the Convergence of SGD with Biased Gradients
We analyze the complexity of biased stochastic gradient methods (SGD), where individual updates are corrupted by deterministic, i. e. biased error terms.
Taming GANs with Lookahead-Minmax
Generative Adversarial Networks are notoriously challenging to train.
Ensemble Distillation for Robust Model Fusion in Federated Learning
In most of the current training schemes the central model is refined by averaging the parameters of the server model and the updated parameters from the client side.
Extrapolation for Large-batch Training in Deep Learning
Deep learning networks are typically trained by Stochastic Gradient Descent (SGD) methods that iteratively improve the model parameters by estimating a gradient on a very small fraction of the training data.
A Unified Theory of Decentralized SGD with Changing Topology and Local Updates
Decentralized stochastic optimization methods have gained a lot of attention recently, mainly because of their cheap per iteration cost, data locality, and their communication-efficiency.
Is Local SGD Better than Minibatch SGD?
We study local SGD (also known as parallel SGD and federated averaging), a natural and frequently used stochastic distributed optimization method.
Advances and Open Problems in Federated Learning
FL embodies the principles of focused data collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches.
SCAFFOLD: Stochastic Controlled Averaging for Federated Learning
We obtain tight convergence rates for FedAvg and prove that it suffers from `client-drift' when the data is heterogeneous (non-iid), resulting in unstable and slow convergence.
The Error-Feedback Framework: Better Rates for SGD with Delayed Gradients and Compressed Communication
We analyze (stochastic) gradient descent (SGD) with delayed updates on smooth quasi-convex and non-convex functions and derive concise, non-asymptotic, convergence rates.
Decentralized Deep Learning with Arbitrary Communication Compression
Decentralized training of deep learning models is a key element for enabling data privacy and on-device learning over networks, as well as for efficient scaling to large compute clusters.
Unified Optimal Analysis of the (Stochastic) Gradient Method
In this note we give a simple proof for the convergence of stochastic gradient (SGD) methods on $\mu$-convex functions under a (milder than standard) $L$-smoothness assumption.
Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication
We (i) propose a novel gossip-based stochastic gradient descent algorithm, CHOCO-SGD, that converges at rate $\mathcal{O}\left(1/(nT) + 1/(T \delta^2 \omega)^2\right)$ for strongly convex objectives, where $T$ denotes the number of iterations and $\delta$ the eigengap of the connectivity matrix.
Error Feedback Fixes SignSGD and other Gradient Compression Schemes
These issues arise because of the biased nature of the sign compression operator.
Efficient Greedy Coordinate Descent for Composite Problems
For these problems we provide (i) the first linear rates of convergence independent of $n$, and show that our greedy update rule provides speedups similar to those obtained in the smooth case.
Sparsified SGD with Memory
Huge scale machine learning problems are nowadays tackled by distributed optimization algorithms, i. e. algorithms that leverage the compute power of many devices for training.
Don't Use Large Mini-Batches, Use Local SGD
Mini-batch stochastic gradient methods (SGD) are state of the art for distributed training of deep neural networks.
Global linear convergence of Newton's method without strong-convexity or Lipschitz gradients
We show that Newton's method converges globally at a linear rate for objective functions whose Hessians are stable.
Local SGD Converges Fast and Communicates Little
Local SGD can also be used for large scale training of deep learning models.
k-SVRG: Variance Reduction for Large Scale Optimization
Variance reduced stochastic gradient (SGD) methods converge significantly faster than the vanilla SGD counterpart.
On Matching Pursuit and Coordinate Descent
Exploiting the connection between the two algorithms, we present a unified analysis of both, providing affine invariant sublinear $\mathcal{O}(1/t)$ rates on smooth objectives and linear convergence on strongly convex objectives.
Safe Adaptive Importance Sampling
Importance sampling has become an indispensable strategy to speed up optimization algorithms for large-scale applications.
Approximate Steepest Coordinate Descent
We propose a new selection rule for the coordinate selection in coordinate descent methods for huge-scale optimization.
