BlockFed: A Novel Federated Learning Framework Based On Hierarchical Weighted Aggregation

Federated learning (FL) has emerged as a promising paradigm for decentralized machine learning, enabling clients to collaboratively train models while keeping their data private. However, a key challenge in FL is the centralized aggregation of model updates, which can lead to inefficiencies and vulnerabilities, especially when data privacy is critical. This study presents a pioneering federated learning framework, BlockFed, which leverages a novel hierarchical aggregation approach to empower clients in collaboratively generating a global model through multiple levels of aggregation. A unique role definition mechanism is integrated to delineate clients' roles and tasks in each learning round. Additionally, BlockFed incorporates the Particle Swarm Optimization (PSO) algorithm to solve an optimization problem for determining optimal weights in the weighted averaging aggregation, enabling faster convergence. To ensure secure and decentralized storage, IPFS and blockchain technologies are used to store local models and their corresponding hash pointers. The efficacy of BlockFed is evaluated using a genomic breast cancer dataset sourced from the GDC portal, achieving a remarkable 98% accuracy for the global model and demonstrating enhanced accuracy and convergence speed over the original framework.