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Decentralized Federated Learning: A Survey and Perspective

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arxiv 2306.01603 v2 pith:DPNI4RSJ submitted 2023-06-02 cs.LG cs.CYcs.DCcs.NI

Liangqi Yuan , Ziran Wang , Lichao Sun , Philip S. Yu , Christopher G. Brinton This is my paper · ORCID
classification cs.LGcs.CYcs.DCcs.NI
keywords communicationdecentralizedlearningperspectivechallengesfederatednetworksurvey
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Federated learning (FL) has been gaining attention for its ability to share knowledge while maintaining user data, protecting privacy, increasing learning efficiency, and reducing communication overhead. Decentralized FL (DFL) is a decentralized network architecture that eliminates the need for a central server in contrast to centralized FL (CFL). DFL enables direct communication between clients, resulting in significant savings in communication resources. In this paper, a comprehensive survey and profound perspective are provided for DFL. First, a review of the methodology, challenges, and variants of CFL is conducted, laying the background of DFL. Then, a systematic and detailed perspective on DFL is introduced, including iteration order, communication protocols, network topologies, paradigm proposals, and temporal variability. Next, based on the definition of DFL, several extended variants and categorizations are proposed with state-of-the-art (SOTA) technologies. Lastly, in addition to summarizing the current challenges in the DFL, some possible solutions and future research directions are also discussed.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Loss-Guided Model Sharing and Local Learning Correction in Decentralized Federated Learning for Crop Disease Classification

    cs.LG 2025-05 reject novelty 4.0 of 10

    The paper proposes a peer-to-peer federated learning framework with validation-loss-based model sharing and a weighted loss-correction term, but the correction term has zero gradient and the experiments show implausib...

  2. GLow -- A Novel, Flower-Based Simulated Gossip Learning Strategy

    cs.LG 2025-01 conditional novelty 4.0 of 10

    GLow implements and simulates fully decentralized gossip learning in the Flower framework, achieving competitive accuracy with centralized and federated baselines on MNIST and CIFAR10.

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