Diffusion-based Decentralized Federated Multi-Task Representation Learning

• URL: http://arxiv.org/abs/2512.23161v1
• Date: Mon, 29 Dec 2025 02:59:24 GMT
• Title: Diffusion-based Decentralized Federated Multi-Task Representation Learning
• Authors: Donghwa Kang, Shana Moothedath,
• Abstract summary: 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.
• 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 to obtain a feature extractor or representation from various different yet related tasks. Despite extensive research on representation learning, decentralized approaches remain relatively underexplored. This work develops a decentralized projected gradient descent-based algorithm for multi-task representation learning. We focus on the problem of multi-task linear regression in which multiple linear regression models share a common, low-dimensional linear representation. We present an alternating projected gradient descent and minimization algorithm for recovering a low-rank feature matrix in a diffusion-based decentralized and federated fashion. We obtain constructive, provable guarantees that provide a lower bound on the required sample complexity and an upper bound on the iteration complexity of our proposed algorithm. We analyze the time and communication complexity of our algorithm and show that it is fast and communication-efficient. We performed numerical simulations to validate the performance of our algorithm and compared it with benchmark algorithms.

Related papers

• Beyond Centralization: Provable Communication Efficient Decentralized Multi-Task Learning [7.147459905796264]
  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.
  arXiv  Detail & Related papers  (2025-12-27T18:44:43Z)
• Communication-Efficient Stochastic Distributed Learning [3.2923780772605595]
  We address distributed learning problems, both non and convex, undirected networks.<n>In particular, we design a novel based on the distributed Alternating Method of Multipliers (MM) to address the challenges of high communication costs.
  arXiv  Detail & Related papers  (2025-01-23T10:05:23Z)
• An Efficient Algorithm for Clustered Multi-Task Compressive Sensing [60.70532293880842]
  Clustered multi-task compressive sensing is a hierarchical model that solves multiple compressive sensing tasks. The existing inference algorithm for this model is computationally expensive and does not scale well in high dimensions. We propose a new algorithm that substantially accelerates model inference by avoiding the need to explicitly compute these covariance matrices.
  arXiv  Detail & Related papers  (2023-09-30T15:57:14Z)
• Robust Online Learning over Networks [1.0249620437941]
  This work specifically targets some prevalent challenges inherent to distributed learning. We apply the Distributed Operator Theoretical (DOT) version of the Alternating Direction Method of Multipliers (ADMM) We prove that if the DOT-ADMM operator is metric subregular, then it converges with a linear rate for a class of convex learning problems.
  arXiv  Detail & Related papers  (2023-09-01T15:18:05Z)
• Stochastic Unrolled Federated Learning [85.6993263983062]
  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.
  arXiv  Detail & Related papers  (2023-05-24T17:26:22Z)
• 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)
• Stabilizing Q-learning with Linear Architectures for Provably Efficient Learning [53.17258888552998]
  This work proposes an exploration variant of the basic $Q$-learning protocol with linear function approximation. We show that the performance of the algorithm degrades very gracefully under a novel and more permissive notion of approximation error.
  arXiv  Detail & Related papers  (2022-06-01T23:26:51Z)
• Deep Equilibrium Assisted Block Sparse Coding of Inter-dependent Signals: Application to Hyperspectral Imaging [71.57324258813675]
  A dataset of inter-dependent signals is defined as a matrix whose columns demonstrate strong dependencies. A neural network is employed to act as structure prior and reveal the underlying signal interdependencies. Deep unrolling and Deep equilibrium based algorithms are developed, forming highly interpretable and concise deep-learning-based architectures.
  arXiv  Detail & Related papers  (2022-03-29T21:00:39Z)
• Distributed Estimation of Sparse Inverse Covariance Matrices [0.7832189413179361]
  We propose a distributed sparse inverse covariance algorithm to learn the network structure in real-time from data collected by distributed agents. Our approach is built on an online graphical alternating minimization algorithm, augmented with a consensus term that allows agents to learn the desired structure cooperatively.
  arXiv  Detail & Related papers  (2021-09-24T15:26:41Z)
• A Low Complexity Decentralized Neural Net with Centralized Equivalence using Layer-wise Learning [49.15799302636519]
  We design a low complexity decentralized learning algorithm to train a recently proposed large neural network in distributed processing nodes (workers) In our setup, the training data is distributed among the workers but is not shared in the training process due to privacy and security concerns. We show that it is possible to achieve equivalent learning performance as if the data is available in a single place.
  arXiv  Detail & Related papers  (2020-09-29T13:08:12Z)
• Statistically Guided Divide-and-Conquer for Sparse Factorization of Large Matrix [2.345015036605934]
  We formulate the statistical problem as a sparse factor regression and tackle it with a divide-conquer approach. In the first stage division, we consider both latent parallel approaches for simplifying the task into a set of co-parsesparserank estimation (CURE) problems. In the second stage division, we innovate a stagewise learning technique, consisting of a sequence simple incremental paths, to efficiently trace out the whole solution of CURE.
  arXiv  Detail & Related papers  (2020-03-17T19:12:21Z)

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.