LiFeChain: Lightweight Blockchain for Secure and Efficient Federated Lifelong Learning in IoT

2025-09-01

AI Summary

To address persistent adversarial attacks, performance degradation due to spatiotemporal data heterogeneity, and unreliable auditing in long-running Federated Lifelong Learning (FLL) for IoT, as well as the prohibitive computational overhead of conventional blockchain solutions, this paper proposes the first lightweight, FLL-tailored blockchain framework. It introduces two core innovations: (i) a Model-association-based Proof-of-Model-Correctness (PoMC) consensus mechanism, and (ii) a Segmented Zero-Knowledge Arbitration (Seg-ZA) protocol—enabling decentralized, tamper-proof knowledge accumulation and fine-grained anomaly auditing. Integrated with a learning-forgetting co-adaptation mechanism, the framework supports bidirectional verification and minimal on-chain disclosure. Experimental evaluation on resource-constrained IoT devices demonstrates significant improvements in long-term attack resilience and model accuracy, while maintaining efficient training throughput and strong scalability.

Technology Category

• Machine Learning: Life-Long and Continual Learning
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Multiagent Systems: Adversarial Agents

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

The expansion of Internet of Things (IoT) devices constantly generates heterogeneous data streams, driving demand for continuous, decentralized intelligence. Federated Lifelong Learning (FLL) provides an ideal solution by incorporating federated and lifelong learning to overcome catastrophic forgetting. The extended lifecycle of FLL in IoT systems increases their vulnerability to persistent attacks, and these risks may be obscured by performance degradation caused by spatial-temporal data heterogeneity. Moreover, this problem is exacerbated by the standard single-server architecture, as its single point of failure makes it difficult to maintain a reliable audit trail for long-term threats. Blockchain provides a tamper-proof foundation for trustworthy FLL systems. Nevertheless, directly applying blockchain to FLL significantly increases computational and retrieval costs with the expansion of the knowledge base, slowing down the training on IoT devices. To address these challenges, we propose LiFeChain, a lightweight blockchain for secure and efficient federated lifelong learning by providing a tamper-resistant ledger with minimal on-chain disclosure and bidirectional verification. To the best of our knowledge, LiFeChain is the first blockchain tailored for FLL. LiFeChain incorporates two complementary mechanisms: the proof-of-model-correlation (PoMC) consensus on the server, which couples learning and unlearning mechanisms to mitigate negative transfer, and segmented zero-knowledge arbitration (Seg-ZA) on the client, which detects and arbitrates abnormal committee behavior without compromising privacy. LiFeChain is designed as a plug-and-play component that can be seamlessly integrated into existing FLL algorithms. Experimental results demonstrate that LiFeChain not only enhances model performance against two long-term attacks but also sustains high efficiency and scalability.

Problem

• Securing federated lifelong learning against persistent attacks in IoT
• Reducing blockchain computational costs for efficient IoT training
• Preventing catastrophic forgetting with decentralized tamper-proof knowledge management

Innovation

• Lightweight blockchain for secure federated lifelong learning
• Proof-of-model-correlation consensus mitigates negative transfer
• Segmented zero-knowledge arbitration detects abnormal behavior