Beyond Centralization: Provable Communication Efficient Decentralized Multi-Task Learning

• URL: http://arxiv.org/abs/2512.22675v1
• Date: Sat, 27 Dec 2025 18:44:43 GMT
• Title: Beyond Centralization: Provable Communication Efficient Decentralized Multi-Task Learning
• Authors: Donghwa Kang, Shana Moothedath,
• Abstract summary: We study decentralized multi-task representation learning in which the features share a low-rank structure.<n>In the decentralized setting, task data are distributed across multiple nodes, and information exchange between nodes is constrained by a communication network.
• Score: 7.147459905796264
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Representation learning is a widely adopted framework for learning in data-scarce environments, aiming to extract common features from related tasks. While centralized approaches have been extensively studied, decentralized methods remain largely underexplored. We study decentralized multi-task representation learning in which the features share a low-rank structure. We consider multiple tasks, each with a finite number of data samples, where the observations follow a linear model with task-specific parameters. In the decentralized setting, task data are distributed across multiple nodes, and information exchange between nodes is constrained by a communication network. The goal is to recover the underlying feature matrix whose rank is much smaller than both the parameter dimension and the number of tasks. We propose a new alternating projected gradient and minimization algorithm with provable accuracy guarantees. We provide comprehensive characterizations of the time, communication, and sample complexities. Importantly, the communication complexity is independent of the target accuracy, which significantly reduces communication cost compared to prior methods. Numerical simulations validate the theoretical analysis across different dimensions and network topologies, and demonstrate regimes in which decentralized learning outperforms centralized federated approaches.

Related papers

• Diffusion-based Decentralized Federated Multi-Task Representation Learning [7.147459905796264]
  This work develops a decentralized projected gradient descent-based algorithm for multi-task representation learning.<n>We focus on the problem of multi-task linear regression in which multiple linear regression models share a common, low-dimensional linear representation.
  arXiv  Detail & Related papers  (2025-12-29T02:59:24Z)
• Robust Decentralized Learning with Local Updates and Gradient Tracking [16.46727164965154]
  We consider decentralized learning as a network of communicating clients or nodes.<n>We propose a decentralized minimax optimization method that employs two important data: local updates and gradient tracking.
  arXiv  Detail & Related papers  (2024-05-02T03:03:34Z)
• Decentralized Learning Strategies for Estimation Error Minimization with Graph Neural Networks [86.99017195607077]
  We address the challenge of sampling and remote estimation for autoregressive Markovian processes in a wireless network with statistically-identical agents.<n>Our goal is to minimize time-average estimation error and/or age of information with decentralized scalable sampling and transmission policies.
  arXiv  Detail & Related papers  (2024-04-04T06:24:11Z)
• Learning Prototype-oriented Set Representations for Meta-Learning [85.19407183975802]
  Learning from set-structured data is a fundamental problem that has recently attracted increasing attention. This paper provides a novel optimal transport based way to improve existing summary networks. We further instantiate it to the cases of few-shot classification and implicit meta generative modeling.
  arXiv  Detail & Related papers  (2021-10-18T09:49:05Z)
• Multi-Source Domain Adaptation for Object Detection [52.87890831055648]
  We propose a unified Faster R-CNN based framework, termed Divide-and-Merge Spindle Network (DMSN) DMSN can simultaneously enhance domain innative and preserve discriminative power. We develop a novel pseudo learning algorithm to approximate optimal parameters of pseudo target subset.
  arXiv  Detail & Related papers  (2021-06-30T03:17:20Z)
• Decentralized Local Stochastic Extra-Gradient for Variational Inequalities [125.62877849447729]
  We consider distributed variational inequalities (VIs) on domains with the problem data that is heterogeneous (non-IID) and distributed across many devices. We make a very general assumption on the computational network that covers the settings of fully decentralized calculations. We theoretically analyze its convergence rate in the strongly-monotone, monotone, and non-monotone settings.
  arXiv  Detail & Related papers  (2021-06-15T17:45:51Z)
• Quasi-Global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data [77.88594632644347]
  Decentralized training of deep learning models is a key element for enabling data privacy and on-device learning over networks. In realistic learning scenarios, the presence of heterogeneity across different clients' local datasets poses an optimization challenge. We propose a novel momentum-based method to mitigate this decentralized training difficulty.
  arXiv  Detail & Related papers  (2021-02-09T11:27:14Z)
• Decentralized Deep Learning using Momentum-Accelerated Consensus [15.333413663982874]
  We consider the problem of decentralized deep learning where multiple agents collaborate to learn from a distributed dataset. We propose and analyze a novel decentralized deep learning algorithm where the agents interact over a fixed communication topology. Our algorithm is based on the heavy-ball acceleration method used in gradient-based protocol.
  arXiv  Detail & Related papers  (2020-10-21T17:39:52Z)
• Dif-MAML: Decentralized Multi-Agent Meta-Learning [54.39661018886268]
  We propose a cooperative multi-agent meta-learning algorithm, referred to as MAML or Dif-MAML. We show that the proposed strategy allows a collection of agents to attain agreement at a linear rate and to converge to a stationary point of the aggregate MAML. Simulation results illustrate the theoretical findings and the superior performance relative to the traditional non-cooperative setting.
  arXiv  Detail & Related papers  (2020-10-06T16:51:09Z)
• Distributed Learning in the Non-Convex World: From Batch to Streaming Data, and Beyond [73.03743482037378]
  Distributed learning has become a critical direction of the massively connected world envisioned by many. This article discusses four key elements of scalable distributed processing and real-time data computation problems. Practical issues and future research will also be discussed.
  arXiv  Detail & Related papers  (2020-01-14T14:11:32Z)

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.