JiRAIYA: A Reputation-Based Hierarchical Federated Learning Framework on Web3

Abstract

Federated Learning(FL) is predominantly deployed in enterprise environments, where limited transparency and restricted auditability hinder broader adoption. Existing FL systems often suffer from opaque aggregation processes, making it unclear which model updates are accepted or discarded. Current mitigation strategies typically rely on external validators introducing additional computational and communication overhead. In this paper, we propose a novel FL framework that leverages existing Web3 technologies to enhance transparency, trust and auditability throughout the training process. The framework adopts a hierarchical architecture in which delegated managers orchestrate the FL training process within their respective federations. To mitigate adversarial and poisoning attacks, a combination of novelty detection and consensus mechanisms were employed. Model updates are encoded and broad casted to all managers, who independently evaluate their validity and those model updates that are approved by the consensus are incorporated into the global model. Additionally, a reputation score based backup mechanism is employed to ensure model generation. Extensive experiments conducted under real world scenarios demonstrate the effectiveness, resilience of the proposed framework, highlighting its potential to enable transparent FL beyond traditional enterprise setting.