mlsanityData to 14 Oct 2026

DAG-AFL:Directed Acyclic Graph-based Asynchronous Federated Learning

arXiv 2507.20571 ↗First posted 28 Jul 2025cs.LG cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

Due to the distributed nature of federated learning (FL), the vulnerability of the global model and the need for coordination among many client devices pose significant challenges. As a promising decentralized, scalable and secure solution, blockchain-based FL methods have attracted widespread attention in recent years. However, traditional consensus mechanisms designed for Proof of Work (PoW) similar to blockchain incur substantial resource consumption and compromise the efficiency of FL, particularly when participating devices are wireless and resource-limited. To address asynchronous client participation and data heterogeneity in FL, while limiting the additional resource overhead introduced by blockchain, we propose the Directed Acyclic Graph-based Asynchronous Federated Learning (DAG-AFL) framework. We develop a tip selection algorithm that considers temporal freshness, node reachability and model accuracy, with a DAG-based trusted verification strategy. Extensive experiments on 3 benchmarking datasets against eight state-of-the-art approaches demonstrate that DAG-AFL significantly improves training efficiency and model accuracy by 22.7% and 6.5% on average, respectively.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 15 of its 22 references that are in the map. First posted on arXiv 28 Jul 2025.

Builds on

15 of 22 references.

References · 15

  1. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  3. 2019Asynchronous Federated Optimizationcited by 267
  4. 2018Blockchained On-Device Federated Learningcited by 106
  5. 2020A Blockchain-based Decentralized Federated Learning Framework with Committee Consensuscited by 84
  6. 2020FedAT: A High-Performance and Communication-Efficient Federated Learning System with Asynchronous Tierscited by 64
  7. 2024Advances and Open Challenges in Federated Foundation Modelscited by 36
  8. 2022Noise-robust Speech Recognition with 10 Minutes Unparalleled In-domain Datacited by 30
  9. 2022Data Heterogeneity-Robust Federated Learning via Group Client Selection in Industrial IoTcited by 25
  10. 2022FedHiSyn: A Hierarchical Synchronous Federated Learning Framework for Resource and Data Heterogeneitycited by 20
  11. 2025Ten Challenging Problems in Federated Foundation Modelscited by 20
  12. 2021PFA: Privacy-preserving Federated Adaptation for Effective Model Personalizationcited by 18
3 more
  1. 2021CSAFL: A Clustered Semi-Asynchronous Federated Learning Frameworkcited by 10
  2. 2022ScaleSFL: A Sharding Solution for Blockchain-Based Federated Learningcited by 4
  3. 2021Towards On-Device Federated Learning: A Direct Acyclic Graph-based Blockchain Approachcited by 3

Built on by

Not cited yet.