Decoupled Edge Data Integrity Verification via Federated Adaptation and Cryptographic Validation

Mobile edge computing (MEC) accelerates Internet of Things (IoT) applications by caching content near end users. However, cached data remains vulnerable to corruption, misleading applications, and eroding user trust. Traditional centralized edge data integrity verification (EDIV) methods adopt challenge-response protocols involving third-party auditors (TPAs), incurring nontrivial computation and communication costs along with privacy concerns. Recent decentralized frameworks leverage federated learning (FL) to train models for initial screening, eliminating TPAs. However, their performance is impeded by computational and data heterogeneity across edge nodes and the substantial overhead of blockchain-based validation. Furthermore, such frameworks exhibit limited precision in localizing corrupted data. To address these challenges, this article introduces the decoupled EDIV (D-EDIV) framework, which explicitly decouples integrity verification into a two-stage mechanism of corruption detection and corruption localization. During Stage 1, D-EDIV executes corruption detection models at edge nodes. A federated adaptation scheme tailors these models to each node’s computational resources and local data distribution. Upon detecting anomalous instances, a fusion strategy correlates network-layer alerts with cached data modification events to generate perblock suspicion scores, prompting the application vendor (AV) to trigger Stage 2. In this stage, the AV conducts targeted localization on potentially corrupted data. In particular, after authenticating data commitments via a digital signature, the AV utilizes suspicion scores to calculate dynamic decision thresholds, guiding Merkle tree traversal and restricting cryptographic validation to high-risk data blocks. Experimental results demonstrate that D-EDIV improves detection accuracy by 3%–12% and reduces computational overhead by 3\times – 8\times, efficiently achieving fine-grained integrity verification in heterogeneous MEC environments.