From Centralized to Decentralized Federated Learning: Theoretical Insights, Privacy Preservation, and Robustness Challenges

• URL: http://arxiv.org/abs/2503.07505v1
• Date: Mon, 10 Mar 2025 16:27:40 GMT
• Title: From Centralized to Decentralized Federated Learning: Theoretical Insights, Privacy Preservation, and Robustness Challenges
• Authors: Qiongxiu Li, Wenrui Yu, Yufei Xia, Jun Pang,
• Abstract summary: Federated Learning (FL) enables collaborative learning without directly sharing individual's raw data.<n>FL can be implemented in either a centralized (server-based) or decentralized (peer-to-peer) manner.
• Score: 6.8109977763829885
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Federated Learning (FL) enables collaborative learning without directly sharing individual's raw data. FL can be implemented in either a centralized (server-based) or decentralized (peer-to-peer) manner. In this survey, we present a novel perspective: the fundamental difference between centralized FL (CFL) and decentralized FL (DFL) is not merely the network topology, but the underlying training protocol: separate aggregation vs. joint optimization. We argue that this distinction in protocol leads to significant differences in model utility, privacy preservation, and robustness to attacks. We systematically review and categorize existing works in both CFL and DFL according to the type of protocol they employ. This taxonomy provides deeper insights into prior research and clarifies how various approaches relate or differ. Through our analysis, we identify key gaps in the literature. In particular, we observe a surprising lack of exploration of DFL approaches based on distributed optimization methods, despite their potential advantages. We highlight this under-explored direction and call for more research on leveraging distributed optimization for federated learning. Overall, this work offers a comprehensive overview from centralized to decentralized FL, sheds new light on the core distinctions between approaches, and outlines open challenges and future directions for the field.

Related papers

• De-VertiFL: A Solution for Decentralized Vertical Federated Learning [7.877130417748362]
  This work introduces De-VertiFL, a novel solution for training models in a decentralized VFL setting. De-VertiFL contributes by introducing a new network architecture distribution, an innovative knowledge exchange scheme, and a distributed federated training process. The results demonstrate that De-VertiFL generally surpasses state-of-the-art methods in F1-score performance, while maintaining a decentralized and privacy-preserving framework.
  arXiv  Detail & Related papers  (2024-10-08T15:31:10Z)
• Provable Privacy Advantages of Decentralized Federated Learning via Distributed Optimization [16.418338197742287]
  Federated learning (FL) emerged as a paradigm designed to improve data privacy by enabling data to reside at its source.<n>Recent findings suggest that decentralized FL does not empirically offer any additional privacy or security benefits over centralized models.<n>We demonstrate that decentralized FL, when deploying distributed optimization, provides enhanced privacy protection.
  arXiv  Detail & Related papers  (2024-07-12T15:01:09Z)
• Vertical Federated Learning for Effectiveness, Security, Applicability: A Survey [67.48187503803847]
  Vertical Federated Learning (VFL) is a privacy-preserving distributed learning paradigm. Recent research has shown promising results addressing various challenges in VFL. This survey offers a systematic overview of recent developments.
  arXiv  Detail & Related papers  (2024-05-25T16:05:06Z)
• Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learning [51.13534069758711]
  Decentralized approaches like blockchain offer a compelling solution by implementing a consensus mechanism among multiple entities. Federated Learning (FL) enables participants to collaboratively train models while safeguarding data privacy. This paper investigates the synergy between blockchain's security features and FL's privacy-preserving model training capabilities.
  arXiv  Detail & Related papers  (2024-03-28T07:08:26Z)
• Towards Understanding Generalization and Stability Gaps between Centralized and Decentralized Federated Learning [57.35402286842029]
  We show that centralized learning always generalizes better than decentralized learning (DFL) We also conduct experiments on several common setups in FL to validate that our theoretical analysis is consistent with experimental phenomena and contextually valid in several general and practical scenarios.
  arXiv  Detail & Related papers  (2023-10-05T11:09:42Z)
• Decentralized Federated Learning: A Survey and Perspective [45.81975053649379]
  Decentralized FL (DFL) is a decentralized network architecture that eliminates the need for a central server. DFL enables direct communication between clients, resulting in significant savings in communication resources.
  arXiv  Detail & Related papers  (2023-06-02T15:12:58Z)
• Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challenges [0.0]
  Federated Learning (FL) has gained relevance in training collaborative models without sharing sensitive data. Decentralized Federated Learning (DFL) emerged to address these concerns by promoting decentralized model aggregation. This article identifies and analyzes the main fundamentals of DFL in terms of federation architectures, topologies, communication mechanisms, security approaches, and key performance indicators.
  arXiv  Detail & Related papers  (2022-11-15T18:51:20Z)
• 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)
• Decentralized Personalized Federated Learning for Min-Max Problems [79.61785798152529]
  This paper is the first to study PFL for saddle point problems encompassing a broader range of optimization problems. We propose new algorithms to address this problem and provide a theoretical analysis of the smooth (strongly) convex-(strongly) concave saddle point problems. Numerical experiments for bilinear problems and neural networks with adversarial noise demonstrate the effectiveness of the proposed methods.
  arXiv  Detail & Related papers  (2021-06-14T10:36:25Z)

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.