Incentive mechanism design in blockchain-based federated learning over edge clouds
type: CreativeWork
description: Federated learning (FL) deployed over edge computing environments is a promising paradigm for preserving data privacy while enabling real-time, decentralized artificial intelligence. However, conventional FL faces critical challenges, including severe communication bottlenecks, vulnerability to single points of failure, and a lack of transparency in model aggregation. While integrating blockchain technology into FL systems mitigates these security risks and ensures traceability, the resulting architecture demands significant computational and communication resources from participating edge servers and client devices. Because these participants are rational and resource-constrained, the success of blockchain-enabled FL relies heavily on robust incentive mechanisms that can motivate contributions to both model training and the blockchain consensus process. This dissertation investigates and designs comprehensive, game-theoretic incentive mechanisms for blockchain-enabled federated edge learning systems, progressively addressing challenges from architectural latency to information asymmetry. First, this research addresses the latency and straggler issues inherent in synchronous FL by proposing an incentive mechanism for a semi-asynchronous blockchain-based federated edge learning system. The resource pricing interaction between edge servers and task publishers is modeled as a two-stage Stackelberg game. To achieve optimal strategies efficiently, an iterative algorithm based on the Alternating Direction Method of Multipliers (ADMM) is developed, ensuring convergence to a unique Nash equilibrium. This foundation is then extended to a more complex, three-tier cloud-edge-client Hierarchical Federated Learning (HFL) architecture. Here, a three-stage Stackelberg game is constructed to model the resource pricing among clients, edge servers, and cloud publishers, which is solved using a hybrid ADMM and backward induction algorithm. Both approaches demonstrate that optimizing u…
abstract: Federated learning (FL) deployed over edge computing environments is a promising paradigm for preserving data privacy while enabling real-time, decentralized artificial intelligence. However, conventional FL faces critical challenges, including severe communication bottlenecks, vulnerability to single points of failure, and a lack of transparency in model aggregation. While integrating blockchain technology into FL systems mitigates these security risks and ensures traceability, the resulting architecture demands significant computational and communication resources from participating edge servers and client devices. Because these participants are rational and resource-constrained, the success of blockchain-enabled FL relies heavily on robust incentive mechanisms that can motivate contributions to both model training and the blockchain consensus process. This dissertation investigates and designs comprehensive, game-theoretic incentive mechanisms for blockchain-enabled federated edge learning systems, progressively addressing challenges from architectural latency to information asymmetry. First, this research addresses the latency and straggler issues inherent in synchronous FL by proposing an incentive mechanism for a semi-asynchronous blockchain-based federated edge learning system. The resource pricing interaction between edge servers and task publishers is modeled as a two-stage Stackelberg game. To achieve optimal strategies efficiently, an iterative algorithm based on the Alternating Direction Method of Multipliers (ADMM) is developed, ensuring convergence to a unique Nash equilibrium. This foundation is then extended to a more complex, three-tier cloud-edge-client Hierarchical Federated Learning (HFL) architecture. Here, a three-stage Stackelberg game is constructed to model the resource pricing among clients, edge servers, and cloud publishers, which is solved using a hybrid ADMM and backward induction algorithm. Both approaches demonstrate that optimizing u…
author: Liu, Xuanzhang
language: eng
keywords: Computer science, Blockchain, Contract theory, Federated learning, Stackelberg game, Computer and information science
license: http://rightsstatements.org/vocab/InC/1.0/
date published: 2026-05
date modified: 2026-07-27T21:20:03Z
provider: Temple University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Temple University. Libraries