FedEP: Tailoring Attention to Heterogeneous Data Distribution with Entropy Pooling for Decentralized Federated Learning

• URL: http://arxiv.org/abs/2410.07678v1
• Date: Thu, 10 Oct 2024 07:39:15 GMT
• Title: FedEP: Tailoring Attention to Heterogeneous Data Distribution with Entropy Pooling for Decentralized Federated Learning
• Authors: Chao Feng, Hongjie Guan, Alberto Huertas Celdrán, Jan von der Assen, Gérôme Bovet, Burkhard Stiller,
• Abstract summary: This paper proposes a novel DFL aggregation algorithm, Federated Entropy Pooling (FedEP) FedEP mitigates the client drift problem by incorporating the statistical characteristics of local distributions instead of any actual data. Experiments have demonstrated that FedEP can achieve faster convergence and show higher test performance than state-of-the-art approaches.
• Score: 8.576433180938004
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Federated Learning (FL) performance is highly influenced by data distribution across clients, and non-Independent and Identically Distributed (non-IID) leads to a slower convergence of the global model and a decrease in model effectiveness. The existing algorithms for solving the non-IID problem are focused on the traditional centralized FL (CFL), where a central server is used for model aggregation. However, in decentralized FL (DFL), nodes lack the overall vision of the federation. To address the non-IID problem in DFL, this paper proposes a novel DFL aggregation algorithm, Federated Entropy Pooling (FedEP). FedEP mitigates the client drift problem by incorporating the statistical characteristics of local distributions instead of any actual data. Prior to training, each client conducts a local distribution fitting using a Gaussian Mixture Model (GMM) and shares the resulting statistical characteristics with its neighbors. After receiving the statistical characteristics shared by its neighbors, each node tries to fit the global data distribution. In the aggregation phase, each node calculates the Kullback-Leibler (KL) divergences of the local data distribution over the fitted global data distribution, giving the weights to generate the aggregated model. Extensive experiments have demonstrated that FedEP can achieve faster convergence and show higher test performance than state-of-the-art approaches.

Related papers

• Distributionally Robust Federated Learning: An ADMM Algorithm [5.65425489838679]
  Federated learning (FL) aims to train machine learning (ML) models collaboratively using decentralized data. Standard FL models often assume that all data come from the same unknown distribution. We propose a novel FL model, Distributionally Robust Federated Learning (DRFL), that applies distributionally robust optimization to overcome the challenges posed by data heterogeneity and distributional ambiguity.
  arXiv  Detail & Related papers  (2025-03-24T08:35:38Z)
• Client Contribution Normalization for Enhanced Federated Learning [4.726250115737579]
  Mobile devices, including smartphones and laptops, generate decentralized and heterogeneous data. Federated Learning (FL) offers a promising alternative by enabling collaborative training of a global model across decentralized devices without data sharing. This paper focuses on data-dependent heterogeneity in FL and proposes a novel approach leveraging mean latent representations extracted from locally trained models.
  arXiv  Detail & Related papers  (2024-11-10T04:03:09Z)
• Enhancing Federated Learning Convergence with Dynamic Data Queue and Data Entropy-driven Participant Selection [13.825031686864559]
  Federated Learning (FL) is a decentralized approach for collaborative model training on edge devices. We present a method to improve convergence in FL by creating a global subset of data on the server and dynamically distributing it across devices. Our approach results in a substantial accuracy boost of approximately 5% for the MNIST dataset, around 18% for CIFAR-10, and 20% for CIFAR-100 with a 10% global subset of data, outperforming the state-of-the-art (SOTA) aggregation algorithms.
  arXiv  Detail & Related papers  (2024-10-23T11:47:04Z)
• An Aggregation-Free Federated Learning for Tackling Data Heterogeneity [50.44021981013037]
  Federated Learning (FL) relies on the effectiveness of utilizing knowledge from distributed datasets. Traditional FL methods adopt an aggregate-then-adapt framework, where clients update local models based on a global model aggregated by the server from the previous training round. We introduce FedAF, a novel aggregation-free FL algorithm.
  arXiv  Detail & Related papers  (2024-04-29T05:55:23Z)
• Rethinking Clustered Federated Learning in NOMA Enhanced Wireless Networks [60.09912912343705]
  This study explores the benefits of integrating the novel clustered federated learning (CFL) approach with non-independent and identically distributed (non-IID) datasets. A detailed theoretical analysis of the generalization gap that measures the degree of non-IID in the data distribution is presented. Solutions to address the challenges posed by non-IID conditions are proposed with the analysis of the properties.
  arXiv  Detail & Related papers  (2024-03-05T17:49:09Z)
• Joint Local Relational Augmentation and Global Nash Equilibrium for Federated Learning with Non-IID Data [36.426794300280854]
  Federated learning (FL) is a distributed machine learning paradigm that needs collaboration between a server and a series of clients with decentralized data. We propose FedRANE, which consists of two main modules, local relational augmentation (LRA) and global Nash equilibrium (GNE) to resolve intra- and inter-client inconsistency simultaneously.
  arXiv  Detail & Related papers  (2023-08-17T06:17:51Z)
• DFedADMM: Dual Constraints Controlled Model Inconsistency for Decentralized Federated Learning [52.83811558753284]
  Decentralized learning (DFL) discards the central server and establishes a decentralized communication network. Existing DFL methods still suffer from two major challenges: local inconsistency and local overfitting.
  arXiv  Detail & Related papers  (2023-08-16T11:22:36Z)
• 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)
• Clustered Data Sharing for Non-IID Federated Learning over Wireless Networks [39.80420645943706]
  Federated Learning (FL) is a distributed machine learning approach to leverage data from the Internet of Things (IoT) Current FL algorithms face the challenges of non-independent and identically distributed (non-IID) data, which causes high communication costs and model accuracy declines. We propose a clustered data sharing framework which spares the partial data from cluster heads to credible associates through device-to-device (D2D) communication.
  arXiv  Detail & Related papers  (2023-02-17T07:11:02Z)
• Integrating Local Real Data with Global Gradient Prototypes for Classifier Re-Balancing in Federated Long-Tailed Learning [60.41501515192088]
  Federated Learning (FL) has become a popular distributed learning paradigm that involves multiple clients training a global model collaboratively. The data samples usually follow a long-tailed distribution in the real world, and FL on the decentralized and long-tailed data yields a poorly-behaved global model. In this work, we integrate the local real data with the global gradient prototypes to form the local balanced datasets.
  arXiv  Detail & Related papers  (2023-01-25T03:18:10Z)
• Semi-Synchronous Personalized Federated Learning over Mobile Edge Networks [88.50555581186799]
  We propose a semi-synchronous PFL algorithm, termed as Semi-Synchronous Personalized FederatedAveraging (PerFedS$2$), over mobile edge networks. We derive an upper bound of the convergence rate of PerFedS2 in terms of the number of participants per global round and the number of rounds. Experimental results verify the effectiveness of PerFedS2 in saving training time as well as guaranteeing the convergence of training loss.
  arXiv  Detail & Related papers  (2022-09-27T02:12:43Z)
• Fine-tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated Learning [86.59588262014456]
  Federated Learning (FL) is an emerging distributed learning paradigm under privacy constraint. We propose a data-free knowledge distillation method to fine-tune the global model in the server (FedFTG) Our FedFTG significantly outperforms the state-of-the-art (SOTA) FL algorithms and can serve as a strong plugin for enhancing FedAvg, FedProx, FedDyn, and SCAFFOLD.
  arXiv  Detail & Related papers  (2022-03-17T11:18:17Z)
• Heterogeneous Federated Learning via Grouped Sequential-to-Parallel Training [60.892342868936865]
  Federated learning (FL) is a rapidly growing privacy-preserving collaborative machine learning paradigm. We propose a data heterogeneous-robust FL approach, FedGSP, to address this challenge. We show that FedGSP improves the accuracy by 3.7% on average compared with seven state-of-the-art approaches.
  arXiv  Detail & Related papers  (2022-01-31T03:15:28Z)
• 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)
• Semi-Supervised Federated Learning with non-IID Data: Algorithm and System Design [42.63120623012093]
  Federated Learning (FL) allows edge devices (or clients) to keep data locally while simultaneously training a shared global model. The distribution of the client's local training data is non-independent identically distributed (non-IID) We present a robust semi-supervised FL system design, where the system aims to solve the problem of data availability and non-IID in FL.
  arXiv  Detail & Related papers  (2021-10-26T03:41:48Z)
• Multi-Center Federated Learning [62.32725938999433]
  Federated learning (FL) can protect data privacy in distributed learning. It merely collects local gradients from users without access to their data. We propose a novel multi-center aggregation mechanism.
  arXiv  Detail & Related papers  (2021-08-19T12:20:31Z)
• Node Selection Toward Faster Convergence for Federated Learning on Non-IID Data [6.040848035935873]
  Federated Learning (FL) is a distributed learning paradigm that enables a large number of resource-limited nodes to collaboratively train a model without data sharing. We proposed Optimal Aggregation algorithm for better aggregation, which finds out the optimal subset of local updates of participating nodes in each global round. We also proposed a Probabilistic Node Selection framework (FedPNS) to dynamically change the probability for each node to be selected.
  arXiv  Detail & Related papers  (2021-05-14T20:56:09Z)
• Fast-Convergent Federated Learning with Adaptive Weighting [6.040848035935873]
  Federated learning (FL) enables resource-constrained edge nodes to collaboratively learn a global model under the orchestration of a central server. We propose Federated Adaptive Weighting (FedAdp) algorithm that aims to accelerate model convergence under the presence of nodes with non-IID dataset. We show that FL training with FedAdp can reduce the number of communication rounds by up to 54.1% on MNIST dataset and up to 45.4% on FashionMNIST dataset.
  arXiv  Detail & Related papers  (2020-12-01T17:35:05Z)

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.