Resilient Federated Chain: Transforming Blockchain Consensus into an Active Defense Layer for Federated Learning

2026-02-25

AI Summary

This work proposes a novel blockchain-based federated learning framework to address the vulnerability of federated learning systems to adversarial attacks, which compromise model integrity and data privacy. Traditional detection methods struggle to adapt to the decentralized nature of federated learning; in response, the proposed framework reconfigures the consensus mechanism to transform redundant computation into a dynamically adjustable active defense layer. By integrating robust aggregation rules with configurable evaluation functions, the system enables adaptive protection against diverse adversarial threats. The architecture incorporates Proof of Federated Learning, Pooled Mining, and an elastic consensus strategy, collectively enhancing robustness and security. Experimental results across multiple image classification tasks under adversarial settings demonstrate significant improvements over existing baselines, validating the framework’s effectiveness in strengthening both system resilience and privacy guarantees.

Technology Category

• Machine Learning: Adversarial Learning & Robustness
• Computer Vision: Adversarial Attacks & Robustness
• Multiagent Systems: Adversarial Agents

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Security and Privacy: Blockchains and distributed ledgers
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing

Abstract

Federated Learning (FL) has emerged as a key paradigm for building Trustworthy AI systems by enabling privacy-preserving, decentralized model training. However, FL is highly susceptible to adversarial attacks that compromise model integrity and data confidentiality, a vulnerability exacerbated by the fact that conventional data inspection methods are incompatible with its decentralized design. While integrating FL with Blockchain technology has been proposed to address some limitations, its potential for mitigating adversarial attacks remains largely unexplored. This paper introduces Resilient Federated Chain (RFC), a novel blockchain-enabled FL framework designed specifically to enhance resilience against such threats. RFC builds upon the existing Proof of Federated Learning architecture by repurposing the redundancy of its Pooled Mining mechanism as an active defense layer that can be combined with robust aggregation rules. Furthermore, the framework introduces a flexible evaluation function in its consensus mechanism, allowing for adaptive defense against different attack strategies. Extensive experimental evaluation on image classification tasks under various adversarial scenarios, demonstrates that RFC significantly improves robustness compared to baseline methods, providing a viable solution for securing decentralized learning environments.

Problem

• Federated Learning
• adversarial attacks
• model integrity
• data confidentiality
• decentralized learning

Innovation

• Resilient Federated Chain
• Blockchain Consensus
• Active Defense Layer
• Robust Aggregation
• Adaptive Evaluation Function