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Found 26 papers, 8 papers with code
Optimizing the Communication-Accuracy Trade-off in Federated Learning with Rate-Distortion Theory
A significant bottleneck in federated learning (FL) is the network communication cost of sending model updates from client devices to the central server.
Diurnal or Nocturnal? Federated Learning of Multi-branch Networks from Periodically Shifting Distributions
Federated learning has been deployed to train machine learning models from decentralized client data on mobile devices in practice.
Convergence and Accuracy Trade-Offs in Federated Learning and Meta-Learning
Using these insights, we are able to compare local update methods based on their convergence/accuracy trade-off, not just their convergence to critical points of the empirical loss.
On the Outsized Importance of Learning Rates in Local Update Methods
We study a family of algorithms, which we refer to as local update methods, that generalize many federated learning and meta-learning algorithms.
Adaptive Federated Optimization
Federated learning is a distributed machine learning paradigm in which a large number of clients coordinate with a central server to learn a model without sharing their own training data.
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.
Federated Learning with Autotuned Communication-Efficient Secure Aggregation
Federated Learning enables mobile devices to collaboratively learn a shared inference model while keeping all the training data on a user's device, decoupling the ability to do machine learning from the need to store the data in the cloud.
Improving Federated Learning Personalization via Model Agnostic Meta Learning
We present FL as a natural source of practical applications for MAML algorithms, and make the following observations.
MLSys: The New Frontier of Machine Learning Systems
Machine learning (ML) techniques are enjoying rapidly increasing adoption.
Towards Federated Learning at Scale: System Design
Federated Learning is a distributed machine learning approach which enables model training on a large corpus of decentralized data.
A Privacy Preserving Randomized Gossip Algorithm via Controlled Noise Insertion
In this work we present a randomized gossip algorithm for solving the average consensus problem while at the same time protecting the information about the initial private values stored at the nodes.
LEAF: A Benchmark for Federated Settings
Modern federated networks, such as those comprised of wearable devices, mobile phones, or autonomous vehicles, generate massive amounts of data each day.
Stochastic, Distributed and Federated Optimization for Machine Learning
Finally, we introduce the concept of Federated Optimization/Learning, where we try to solve the machine learning problems without having data stored in any centralized manner.
Privacy Preserving Randomized Gossip Algorithms
In this work we present three different randomized gossip algorithms for solving the average consensus problem while at the same time protecting the information about the initial private values stored at the nodes.
Optimization and Control
Randomized Distributed Mean Estimation: Accuracy vs Communication
We consider the problem of estimating the arithmetic average of a finite collection of real vectors stored in a distributed fashion across several compute nodes subject to a communication budget constraint.
Federated Learning: Strategies for Improving Communication Efficiency
We consider learning algorithms for this setting where on each round, each client independently computes an update to the current model based on its local data, and communicates this update to a central server, where the client-side updates are aggregated to compute a new global model.
Federated Optimization: Distributed Machine Learning for On-Device Intelligence
We refer to this setting as Federated Optimization.
AIDE: Fast and Communication Efficient Distributed Optimization
It is well known that DANE algorithm does not match the communication complexity lower bounds.
Distributed Optimization with Arbitrary Local Solvers
To this end, we present a framework for distributed optimization that both allows the flexibility of arbitrary solvers to be used on each (single) machine locally, and yet maintains competitive performance against other state-of-the-art special-purpose distributed methods.
StopWasting My Gradients: Practical SVRG
We present and analyze several strategies for improving the performance ofstochastic variance-reduced gradient (SVRG) methods.
Federated Optimization:Distributed Optimization Beyond the Datacenter
We refer to this setting as Federated Optimization.
Stop Wasting My Gradients: Practical SVRG
We present and analyze several strategies for improving the performance of stochastic variance-reduced gradient (SVRG) methods.
Mini-Batch Semi-Stochastic Gradient Descent in the Proximal Setting
Our method first performs a deterministic step (computation of the gradient of the objective function at the starting point), followed by a large number of stochastic steps.
mS2GD: Mini-Batch Semi-Stochastic Gradient Descent in the Proximal Setting
Our method first performs a deterministic step (computation of the gradient of the objective function at the starting point), followed by a large number of stochastic steps.
One-Shot-Learning Gesture Recognition using HOG-HOF Features
We use RGB and depth images and combine appearance (Histograms of Oriented Gradients) and motion descriptors (Histogram of Optical Flow) for parallel temporal segmentation and recognition.
Semi-Stochastic Gradient Descent Methods
The total work needed for the method to output an $\varepsilon$-accurate solution in expectation, measured in the number of passes over data, or equivalently, in units equivalent to the computation of a single gradient of the loss, is $O((\kappa/n)\log(1/\varepsilon))$, where $\kappa$ is the condition number.
