mlsanityData to 14 Oct 2026

IronForge: An Open, Secure, Fair, Decentralized Federated Learning

arXiv 2301.04006 ↗First posted 7 Jan 2023cs.LG cs.CRcited by 15

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Abstract (arXiv)

Federated learning (FL) provides an effective machine learning (ML) architecture to protect data privacy in a distributed manner. However, the inevitable network asynchrony, the over-dependence on a central coordinator, and the lack of an open and fair incentive mechanism collectively hinder its further development. We propose textscIronForge, a new generation of FL framework, that features a Directed Acyclic Graph (DAG)-based data structure and eliminates the need for central coordinators to achieve fully decentralized operations. textscIronForge runs in a public and open network, and launches a fair incentive mechanism by enabling state consistency in the DAG, so that the system fits in networks where training resources are unevenly distributed. In addition, dedicated defense strategies against prevalent FL attacks on incentive fairness and data privacy are presented to ensure the security of textscIronForge. Experimental results based on a newly developed testbed FLSim highlight the superiority of textscIronForge to the existing prevalent FL frameworks under various specifications in performance, fairness, and security. To the best of our knowledge, textscIronForge is the first secure and fully decentralized FL framework that can be applied in open networks with realistic network and training settings.

Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). Builds on 23 of its 41 references that are in the map. First posted on arXiv 7 Jan 2023.

Builds on

23 of 41 references.

References · 23

  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. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,475
  4. 2016Practical Black-Box Attacks against Machine Learningcited by 903
  5. 2018How To Backdoor Federated Learningcited by 765
  6. 2016Stealing Machine Learning Models via Prediction APIscited by 619
  7. 2019Robust and Communication-Efficient Federated Learning from Non-IID Datacited by 419
  8. 2018Analyzing Federated Learning through an Adversarial Lenscited by 351
  9. 2018Knockoff Nets: Stealing Functionality of Black-Box Modelscited by 292
  10. 2019Asynchronous Federated Optimizationcited by 267
  11. 2018Blockchained On-Device Federated Learningcited by 106
  12. 2020Collaborative Fairness in Federated Learningcited by 95
11 more
  1. 2019Reliable Federated Learning for Mobile Networkscited by 92
  2. 2021Proof-of-Learning: Definitions and Practicecited by 84
  3. 2019SAFA: a Semi-Asynchronous Protocol for Fast Federated Learning with Low Overheadcited by 82
  4. 2021Federated Learning Meets Blockchain in Edge Computing: Opportunities and Challengescited by 76
  5. 2018Copycat CNN: Stealing Knowledge by Persuading Confession with Random Non-Labeled Datacited by 70
  6. 2020A Secure Federated Learning Framework for 5G Networkscited by 32
  7. 2020When Federated Learning Meets Blockchain: A New Distributed Learning Paradigmcited by 31
  8. 2021"Adversarial Examples" for Proof-of-Learningcited by 20
  9. 2022ScaleSFL: A Sharding Solution for Blockchain-Based Federated Learningcited by 4
  10. 2022Dataset Obfuscation: Its Applications to and Impacts on Edge Machine Learningcited by 4
  11. 2021Towards On-Device Federated Learning: A Direct Acyclic Graph-based Blockchain Approachcited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 5; 2024: 6; 2025: 2; 2026: 2.

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  1. 2023Data and Model Poisoning Backdoor Attacks on Wireless Federated Learning, and the Defense Mechanisms: A Comprehensive Surveycited by 18
  2. 2024Noiseless Privacy-Preserving Decentralized Learningcited by 9
  3. 2023Advancements in Federated Learning: Models, Methods, and Privacycited by 7
  4. 2023Blockchained Federated Learning for Internet of Things: A Comprehensive Surveycited by 5
  5. 2024Is Your AI Truly Yours? Leveraging Blockchain for Copyrights, Provenance, and Lineagecited by 4
  6. 2023Blockchain-Empowered Trustworthy Data Sharing: Fundamentals, Applications, and Challengescited by 3
  7. 2024BlockFUL: Enabling Unlearning in Blockchained Federated Learningcited by 3
  8. 2024Smart Sampling: Helping from Friendly Neighbors for Decentralized Federated Learningcited by 2
  9. 2024Parallel Unlearning in Inherited Model Networkscited by 1
  10. 2025Carpe Diem: Critical Learning Period-Aware Contract-Based Incentives for Federated Learningcited by 1
  11. 2026ZK-HybridFL: Zero-Knowledge Proof-Enhanced Hybrid Ledger for Federated Learningcited by 1
  12. 2023Distributed Trust Through the Lens of Software Architecturecited by 0
3 more
  1. 2024Alpha and Prejudice: Improving α-sized Worst-case Fairness via Intrinsic Reweightingcited by 0
  2. 2025PoLO: Proof-of-Learning and Proof-of-Ownership at Once with Chained Watermarkingcited by 0
  3. 2026Secure Decentralized Federated Learning via Gossip and Virtual Votingcited by 0