mlsanityData to 14 Oct 2026

DAG-ACFL: Asynchronous Clustered Federated Learning based on DAG-DLT

arXiv 2308.13158 ↗First posted 25 Aug 2023cs.LG cs.AI cs.DCcited by 0

Not on the map. Why?

Abstract (arXiv)

Federated learning (FL) aims to collaboratively train a global model while ensuring client data privacy. However, FL faces challenges from the non-IID data distribution among clients. Clustered FL (CFL) has emerged as a promising solution, but most existing CFL frameworks adopt synchronous frameworks lacking asynchrony. An asynchronous CFL framework called SDAGFL based on directed acyclic graph distributed ledger techniques (DAG-DLT) was proposed, but its complete decentralization leads to high communication and storage costs. We propose DAG-ACFL, an asynchronous clustered FL framework based on directed acyclic graph distributed ledger techniques (DAG-DLT). We first detail the components of DAG-ACFL. A tip selection algorithm based on the cosine similarity of model parameters is then designed to aggregate models from clients with similar distributions. An adaptive tip selection algorithm leveraging change-point detection dynamically determines the number of selected tips. We evaluate the clustering and training performance of DAG-ACFL on multiple datasets and analyze its communication and storage costs. Experiments show the superiority of DAG-ACFL in asynchronous clustered FL. By combining DAG-DLT with clustered FL, DAG-ACFL realizes robust, decentralized and private model training with efficient performance.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 18 of its 40 references that are in the map. First posted on arXiv 25 Aug 2023.

Builds on

18 of 40 references.

References · 18

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,538
  3. 2018Federated Learning with Non-IID Datacited by 1,108
  4. 2019Towards Federated Learning at Scale: System Designcited by 832
  5. 2016Federated Optimization: Distributed Machine Learning for On-Device Intelligencecited by 564
  6. 2020An Efficient Framework for Clustered Federated Learningcited by 511
  7. 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 503
  8. 2019Robust and Communication-Efficient Federated Learning from Non-IID Datacited by 419
  9. 2020Federated learning with hierarchical clustering of local updates to improve training on non-IID datacited by 223
  10. 2021Multi-Center Federated Learning: Clients Clustering for Better Personalizationcited by 113
  11. 2021Federated Learning Meets Blockchain in Edge Computing: Opportunities and Challengescited by 76
  12. 2018Hyperledger Fabric: A Distributed Operating System for Permissioned Blockchainscited by 49
6 more
  1. 2021Flexible Clustered Federated Learning for Client-Level Data Distribution Shiftcited by 47
  2. 2022On the Convergence of Clustered Federated Learningcited by 31
  3. 2020Device Heterogeneity in Federated Learning: A Superquantile Approachcited by 21
  4. 2021Federated Learning with Taskonomy for Non-IID Datacited by 21
  5. 2021A Systematic Survey of Blockchained Federated Learningcited by 15
  6. 2021Implicit Model Specialization through DAG-based Decentralized Federated Learningcited by 4

Built on by

Not cited yet.