Blockchain-Based Federated Learning

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9 papers0 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

SCOPE-FL: A Strategy-proof Chain-based Optimal pareto efficient Federated Learning System
Jun 16, 2026Seyed Salar Ghazi, Kaiwen Zhang, Mehdi feizi +1Hierarchical Federated Learning Mechanism Design
Department of Software and IT Engineering, École de Technologie Supérieure (ÉTS), Montréal, QC H3C 1K3, Canada · Department of Economics, Faculty of Economics and Administrative Sciences, Ferdowsi University of Mashhad, Mashhad, Iran · Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON M5S 3G4, Canada
TITAN-FedAnil+: Trust-Based Adaptive Blockchain Federated Learning for Resource-Constrained Intelligent Enterprises
Jun 3, 2026Muhammad Hadi, Muhammad Jahangir, Talha Shafique +1Byzantine-Robust Federated Learning Non-IID Federated Learning
School of Electrical Engineering and Computer Science (SEECS) National University of Sciences and Technology (NUST) Islamabad, Pakistan
ChainLearn: A Blockchain-Based Capacity-Aware Framework for Federated Ensemble Learning
May 23, 2026Karan Sharma, Aditya Tripathi, Rahul Mishra +1Federated Learning Aggregation Blockchain-Based Federated Learning
DA-IICT Gandhinagar, India · IIT Patna Patna, India
Automated Byzantine-Resilient Clustered Decentralized Federated Learning for Battery Intelligence in Connected EVs
May 20, 2026Mouhamed Amine Bouchiha, Abdelaziz Amara Korba, Yacine Ghamri-DoudaneByzantine-Robust Federated Learning Decentralized Learning
SAMOVAR, T´el´ecom SudParis, Paris, France · Department of Computer Science, German University of Technology in Oman (GUtech), Muscat, Oman · L3i, La Rochelle University, La Rochelle, France
Federated Learning over Blockchain-Enabled Cloud Infrastructure
Apr 21, 2026Saloni Garg, Amit Sagtani, Kamal Kant HiranPrivacy-Preserving ML Blockchain-Based Federated Learning
Adobe Research San Jose, USA · San Francisco State University San Francisco, USA · Sir Padampat Singhania University Udaipur, India
SettleFL: Trustless and Scalable Reward Settlement Protocol for Federated Learning on Permissionless Blockchains (Extended version)
Feb 26, 2026Shuang Liang, Yang Hua, Linshan Jiang +4Incentive Mechanisms for Federated Learning Blockchain-Based Federated Learning
Shanghai Jiao Tong University · Queen's University Belfast · Queen’s University Belfast +1
Resilient Federated Chain: Transforming Blockchain Consensus into an Active Defense Layer for Federated Learning
Feb 25, 2026Mario García-Márquez, Nuria Rodríguez-Barroso, M. Victoria Luzón +1Blockchain-Based Federated Learning Federated Learning
Department of Computer Science and Artificial Intelligence, Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) University of Granada Granada · Department of Software Engineering, Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) University of Granada Granada
PenTiDef: Decentralized Federated Intrusion Detection System with Differential Privacy and Latent-Space Defense via Blockchain Coordination in IIoT
Feb 20, 2026Phan The Duy, Nghi Hoang Khoa, Nguyen Tran Anh Quan +3Blockchain-Based Federated Learning IoT Intrusion Detection
Information Security Lab, University of Information Technology, Ho Chi Minh City, Vietnam. · Vietnam National University, Ho Chi Minh City, Vietnam. · VNU-HCM Information Security Center, Ho Chi Minh City, Vietnam.
Trust-free Personalized Decentralized Learning
Oct 15, 2024Yawen Li, Yan Li, Junping Du +3Decentralized Learning Blockchain-Based Federated Learning
Beijing University of Posts and Telecommunications, Beijing, China

Jun 16, 2026J/K move · Enter open · S save

SCOPE-FL: A Strategy-proof Chain-based Optimal pareto efficient Federated Learning System

Seyed Salar Ghazi, Kaiwen Zhang, Mehdi feizi, Hans-Arno Jacobsen

Department of Software and IT Engineering, École de Technologie Supérieure (ÉTS), Montréal, QC H3C 1K3, Canada · Department of Economics, Faculty of Economics and Administrative Sciences, Ferdowsi University of Mashhad, Mashhad, Iran · Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON M5S 3G4, Canada

Hierarchical Federated Learning Mechanism Design Client Selection in Federated Learning Blockchain-Based Federated Learning Federated Learning

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Hierarchical Federated Learning (HFL) enables scalable collaborative model training across distributed devices while preserving data privacy. However, existing HFL client selection mechanisms suffer from a fundamental strategic inefficiency. By prioritizing stability over Pareto efficiency (PE), they produce suboptimal resource allocations, and without strategy proofness (SP), participants are incentivized to misrepresent their true preferences, both failures degrading system overall welfare in the Pareto sense in practice. To address it, we propose SCOPE-FL (Strategy-proof Chain-based Optimal pareto efficient Federated Learning), a synchronous HFL framework that formulates client selection as a two-sided school choice problem solved through the Top Trading Cycle (TTC) algorithm that simultaneously guarantees PE and SP. For reward distribution, SCOPE-FL employs a scalable Shapley value approximation based on One-Round Reconstruction (OR), ensuring compensation proportional to each client's contribution. The entire mechanism executes via blockchain smart contracts, providing the tamper-proof environment required for the SP guarantees to hold in practice. A comprehensive evaluation on MNIST, Fashion-MNIST, and CIFAR-10 demonstrates that SCOPE-FL outperforms state-of-the-art approaches, including DA, IAS, and other methods across model accuracy, convergence rate, and reward efficiency, while achieving communication latency comparable to DA and blockchain overhead significantly lower than DA at scale.