Smart Information Exchange for Unsupervised Federated Learning via Reinforcement Learning

• URL: http://arxiv.org/abs/2402.09629v1
• Date: Thu, 15 Feb 2024 00:14:41 GMT
• Title: Smart Information Exchange for Unsupervised Federated Learning via Reinforcement Learning
• Authors: Seohyun Lee, Anindya Bijoy Das, Satyavrat Wagle, Christopher G. Brinton
• Abstract summary: We propose an approach to create an optimal graph for data transfer using Reinforcement Learning. The goal is to form links that will provide the most benefit considering the environment's constraints. Numerical analysis shows the advantages in terms of convergence speed and straggler resilience of the proposed method.
• Score: 11.819765040106185
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: One of the main challenges of decentralized machine learning paradigms such as Federated Learning (FL) is the presence of local non-i.i.d. datasets. Device-to-device transfers (D2D) between distributed devices has been shown to be an effective tool for dealing with this problem and robust to stragglers. In an unsupervised case, however, it is not obvious how data exchanges should take place due to the absence of labels. In this paper, we propose an approach to create an optimal graph for data transfer using Reinforcement Learning. The goal is to form links that will provide the most benefit considering the environment's constraints and improve convergence speed in an unsupervised FL environment. Numerical analysis shows the advantages in terms of convergence speed and straggler resilience of the proposed method to different available FL schemes and benchmark datasets.

Related papers

• Robust Decentralized Learning with Local Updates and Gradient Tracking [16.46727164965154]
  We consider decentralized learning as a network of communicating clients or nodes. 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)
• Topology Learning for Heterogeneous Decentralized Federated Learning over Unreliable D2D Networks [25.672506041978615]
  Decentralized federated learning (DFL) has attracted significant interest in wireless device-to-device (D2D) networks. We conduct a theoretical convergence analysis for DFL and derive a convergence bound. We develop a novel Topology Learning method considering the Representation Discrepancy and Unreliable Links in DFL, named ToLRDUL.
  arXiv  Detail & Related papers  (2023-12-21T07:01:18Z)
• Over-the-Air Federated Learning and Optimization [52.5188988624998]
  We focus on Federated learning (FL) via edge-the-air computation (AirComp) We describe the convergence of AirComp-based FedAvg (AirFedAvg) algorithms under both convex and non- convex settings. For different types of local updates that can be transmitted by edge devices (i.e., model, gradient, model difference), we reveal that transmitting in AirFedAvg may cause an aggregation error. In addition, we consider more practical signal processing schemes to improve the communication efficiency and extend the convergence analysis to different forms of model aggregation error caused by these signal processing schemes.
  arXiv  Detail & Related papers  (2023-10-16T05:49:28Z)
• Enabling Quartile-based Estimated-Mean Gradient Aggregation As Baseline for Federated Image Classifications [5.5099914877576985]
  Federated Learning (FL) has revolutionized how we train deep neural networks by enabling decentralized collaboration while safeguarding sensitive data and improving model performance. This paper introduces an innovative solution named Estimated Mean Aggregation (EMA) that not only addresses these challenges but also provides a fundamental reference point as a $mathsfbaseline$ for advanced aggregation techniques in FL systems.
  arXiv  Detail & Related papers  (2023-09-21T17:17:28Z)
• Adaptive Model Pruning and Personalization for Federated Learning over Wireless Networks [72.59891661768177]
  Federated learning (FL) enables distributed learning across edge devices while protecting data privacy. We consider a FL framework with partial model pruning and personalization to overcome these challenges. This framework splits the learning model into a global part with model pruning shared with all devices to learn data representations and a personalized part to be fine-tuned for a specific device.
  arXiv  Detail & Related papers  (2023-09-04T21:10:45Z)
• Analysis and Optimization of Wireless Federated Learning with Data Heterogeneity [72.85248553787538]
  This paper focuses on performance analysis and optimization for wireless FL, considering data heterogeneity, combined with wireless resource allocation. We formulate the loss function minimization problem, under constraints on long-term energy consumption and latency, and jointly optimize client scheduling, resource allocation, and the number of local training epochs (CRE) Experiments on real-world datasets demonstrate that the proposed algorithm outperforms other benchmarks in terms of the learning accuracy and energy consumption.
  arXiv  Detail & Related papers  (2023-08-04T04:18:01Z)
• FLARE: Detection and Mitigation of Concept Drift for Federated Learning based IoT Deployments [2.7776688429637466]
  FLARE is a lightweight dual-scheduler FL framework that conditionally transfers training data and deploys models between edge and sensor endpoints. We show that FLARE can significantly reduce the amount of data exchanged between edge and sensor nodes compared to fixed-interval scheduling methods. It can successfully detect concept drift reactively with at least a 16x reduction in latency.
  arXiv  Detail & Related papers  (2023-05-15T10:09:07Z)
• Magnitude Matters: Fixing SIGNSGD Through Magnitude-Aware Sparsification in the Presence of Data Heterogeneity [60.791736094073]
  Communication overhead has become one of the major bottlenecks in the distributed training of deep neural networks. We propose a magnitude-driven sparsification scheme, which addresses the non-convergence issue of SIGNSGD. The proposed scheme is validated through experiments on Fashion-MNIST, CIFAR-10, and CIFAR-100 datasets.
  arXiv  Detail & Related papers  (2023-02-19T17:42:35Z)
• Online Data Selection for Federated Learning with Limited Storage [53.46789303416799]
  Federated Learning (FL) has been proposed to achieve distributed machine learning among networked devices. The impact of on-device storage on the performance of FL is still not explored. In this work, we take the first step to consider the online data selection for FL with limited on-device storage.
  arXiv  Detail & Related papers  (2022-09-01T03:27:33Z)
• Local Learning Matters: Rethinking Data Heterogeneity in Federated Learning [61.488646649045215]
  Federated learning (FL) is a promising strategy for performing privacy-preserving, distributed learning with a network of clients (i.e., edge devices)
  arXiv  Detail & Related papers  (2021-11-28T19:03:39Z)
• Communication-Efficient Hierarchical Federated Learning for IoT Heterogeneous Systems with Imbalanced Data [42.26599494940002]
  Federated learning (FL) is a distributed learning methodology that allows multiple nodes to cooperatively train a deep learning model. This paper studies the potential of hierarchical FL in IoT heterogeneous systems. It proposes an optimized solution for user assignment and resource allocation on multiple edge nodes.
  arXiv  Detail & Related papers  (2021-07-14T08:32:39Z)

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.