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Found 9 papers, 1 papers with code
Compressed Federated Reinforcement Learning with a Generative Model
Addressing this challenge, federated reinforcement learning (FedRL) has emerged, wherein agents collaboratively learn a single policy by aggregating local estimations.
Distributed Momentum Methods Under Biased Gradient Estimations
In this work, we establish non-asymptotic convergence bounds on distributed momentum methods under biased gradient estimation on both general non-convex and $\mu$-PL non-convex problems.
On the Convergence of Federated Learning Algorithms without Data Similarity
In this paper, we present a novel and unified framework for analyzing the convergence of federated learning algorithms without the need for data similarity conditions.
Clip21: Error Feedback for Gradient Clipping
Motivated by the increasing popularity and importance of large-scale training under differential privacy (DP) constraints, we study distributed gradient methods with gradient clipping, i. e., clipping applied to the gradients computed from local information at the nodes.
Balancing Privacy and Performance for Private Federated Learning Algorithms
Our findings reveal a direct correlation between the optimal number of local steps, communication rounds, and a set of variables, e. g the DP privacy budget and other problem parameters, specifically in the context of strongly convex optimization.
A flexible framework for communication-efficient machine learning: from HPC to IoT
With the increasing scale of machine learning tasks, it has become essential to reduce the communication between computing nodes.
Compressed Gradient Methods with Hessian-Aided Error Compensation
Several gradient compression techniques have been proposed to reduce the communication load at the price of a loss in solution accuracy.
The Convergence of Sparsified Gradient Methods
Distributed training of massive machine learning models, in particular deep neural networks, via Stochastic Gradient Descent (SGD) is becoming commonplace.
Distributed learning with compressed gradients
Asynchronous computation and gradient compression have emerged as two key techniques for achieving scalability in distributed optimization for large-scale machine learning.
