Internet of Things (IoT) devices constantly generate heterogeneous data streams, driving demand for continuous, decentralized intelligence. Federated Lifelong Learning (FLL) provides an ideal solution by incorporating federated learning and lifelong learning. However, the extended lifecycle of FLL in IoT systems increases their vulnerability to persistent attacks. This problem is exacerbated by the single point of failure. Furthermore, the single point of trust created by the central server hinders reliable auditing for long-term threats. Blockchain technology provides a tamper-proof foundation for trustworthy FLL. Nevertheless, directly applying blockchain to FLL significantly increases computational and retrieval costs with the expansion of the knowledge base, slowing down the training on resource-constrained IoT devices. To address these challenges, we propose LiFeChain, a lightweight blockchain for secure and efficient federated lifelong learning with minimal on-chain disclosure and bidirectional verification. LiFeChain is the first blockchain tailored for FLL. It 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) at the client, which detects and arbitrates abnormal committee behavior without compromising privacy. LiFeChain is a plug-and-play component that can be seamlessly integrated into existing FLL algorithms for IoT applications. To demonstrate its practicality and performance, we implement LiFeChain in representative FLL algorithms with Hyperledger Fabric under 6 attacks. Theoretical analysis and extensive evaluations demonstrate that LiFeChain effectively mitigates long-term attacks, and significantly reduces latency and storage overhead compared to state-of-the-art blockchain solutions.

翻译：物联网设备持续生成异构数据流，催生了对连续分布式智能的需求。联邦持续学习（FLL）通过融合联邦学习与持续学习，为这一问题提供了理想解决方案。然而，FLL在物联网系统中的长生命周期使其更易遭受持续性攻击，而单点故障问题进一步加剧了这一风险。此外，由中心服务器造成的单点信任障碍阻碍了对长期威胁的可信审计。区块链技术为可信FLL提供了防篡改基础，但直接将其应用于FLL会导致知识库扩展时计算与检索成本剧增，从而拖慢资源受限物联网设备的训练过程。为解决上述挑战，我们提出LiFeChain——一种通过最小化链上披露与双向验证实现安全高效联邦持续学习的轻量级区块链。LiFeChain是首个专为FLL设计的区块链，集成了两种互补机制：服务器端的模型相关性证明（PoMC）共识，通过耦合学习与卸载机制缓解负迁移；客户端的分段零知识仲裁（Seg-ZA），在不泄露隐私的前提下检测并仲裁异常委员会行为。LiFeChain作为即插即用组件，可无缝集成至现有面向物联网应用的FLL算法中。为验证其实用性与性能，我们在6种攻击场景下基于Hyperledger Fabric在代表性FLL算法中实现LiFeChain。理论分析与广泛评估表明，LiFeChain能有效缓解长期攻击，相较于现有区块链解决方案显著降低延迟与存储开销。