Stochastic Unrolled Federated Learning

• URL: http://arxiv.org/abs/2305.15371v2
• Date: Tue, 6 Feb 2024 19:27:52 GMT
• Title: Stochastic Unrolled Federated Learning
• Authors: Samar Hadou, Navid NaderiAlizadeh, and Alejandro Ribeiro
• Abstract summary: We introduce UnRolled Federated learning (SURF), a method that expands algorithm unrolling to federated learning. Our proposed method tackles two challenges of this expansion, namely the need to feed whole datasets to the unrolleds and the decentralized nature of federated learning.
• Score: 85.6993263983062
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Algorithm unrolling has emerged as a learning-based optimization paradigm that unfolds truncated iterative algorithms in trainable neural-network optimizers. We introduce Stochastic UnRolled Federated learning (SURF), a method that expands algorithm unrolling to federated learning in order to expedite its convergence. Our proposed method tackles two challenges of this expansion, namely the need to feed whole datasets to the unrolled optimizers to find a descent direction and the decentralized nature of federated learning. We circumvent the former challenge by feeding stochastic mini-batches to each unrolled layer and imposing descent constraints to guarantee its convergence. We address the latter challenge by unfolding the distributed gradient descent (DGD) algorithm in a graph neural network (GNN)-based unrolled architecture, which preserves the decentralized nature of training in federated learning. We theoretically prove that our proposed unrolled optimizer converges to a near-optimal region infinitely often. Through extensive numerical experiments, we also demonstrate the effectiveness of the proposed framework in collaborative training of image classifiers.

Related papers

• Adaptive Federated Learning Over the Air [108.62635460744109]
  We propose a federated version of adaptive gradient methods, particularly AdaGrad and Adam, within the framework of over-the-air model training. Our analysis shows that the AdaGrad-based training algorithm converges to a stationary point at the rate of $mathcalO( ln(T) / T 1 - frac1alpha ).
  arXiv  Detail & Related papers  (2024-03-11T09:10:37Z)
• Robust Stochastically-Descending Unrolled Networks [85.6993263983062]
  Deep unrolling is an emerging learning-to-optimize method that unrolls a truncated iterative algorithm in the layers of a trainable neural network. We show that convergence guarantees and generalizability of the unrolled networks are still open theoretical problems. We numerically assess unrolled architectures trained under the proposed constraints in two different applications.
  arXiv  Detail & Related papers  (2023-12-25T18:51:23Z)
• Faster Adaptive Federated Learning [84.38913517122619]
  Federated learning has attracted increasing attention with the emergence of distributed data. In this paper, we propose an efficient adaptive algorithm (i.e., FAFED) based on momentum-based variance reduced technique in cross-silo FL.
  arXiv  Detail & Related papers  (2022-12-02T05:07:50Z)
• On the Convergence of Distributed Stochastic Bilevel Optimization Algorithms over a Network [55.56019538079826]
  Bilevel optimization has been applied to a wide variety of machine learning models. Most existing algorithms restrict their single-machine setting so that they are incapable of handling distributed data. We develop novel decentralized bilevel optimization algorithms based on a gradient tracking communication mechanism and two different gradients.
  arXiv  Detail & Related papers  (2022-06-30T05:29:52Z)
• Asynchronous Distributed Reinforcement Learning for LQR Control via Zeroth-Order Block Coordinate Descent [7.6860514640178]
  We propose a novel zeroth-order optimization algorithm for distributed reinforcement learning. It allows each agent to estimate its local gradient by cost evaluation independently, without use of any consensus protocol.
  arXiv  Detail & Related papers  (2021-07-26T18:11:07Z)
• Sample-based and Feature-based Federated Learning via Mini-batch SSCA [18.11773963976481]
  This paper investigates sample-based and feature-based federated optimization. We show that the proposed algorithms can preserve data privacy through the model aggregation mechanism. We also show that the proposed algorithms converge to Karush-Kuhn-Tucker points of the respective federated optimization problems.
  arXiv  Detail & Related papers  (2021-04-13T08:23:46Z)
• Decentralized Statistical Inference with Unrolled Graph Neural Networks [26.025935320024665]
  We propose a learning-based framework, which unrolls decentralized optimization algorithms into graph neural networks (GNNs) By minimizing the recovery error via end-to-end training, this learning-based framework resolves the model mismatch issue. Our convergence analysis reveals that the learned model parameters may accelerate the convergence and reduce the recovery error to a large extent.
  arXiv  Detail & Related papers  (2021-04-04T07:52:34Z)
• Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural Networks [50.42141893913188]
  We study a distributed variable for large-scale AUC for a neural network as with a deep neural network. Our model requires a much less number of communication rounds and still a number of communication rounds in theory. Our experiments on several datasets show the effectiveness of our theory and also confirm our theory.
  arXiv  Detail & Related papers  (2020-05-05T18:08:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.