Federated Learning with Adaptive Regularization for Efficient Edge Data Corruption Detection in Edge Intelligence

Edge intelligence is an emerging distributed computing paradigm that has been driven by the rapid proliferation of Internet of Things (IoT) devices, along with the advancements in edge computing and artificial intelligence. With latency-sensitive data commonly cached across multiple Edge Servers (ESs), efficient Edge Data Integrity Verification (EDIV) has become increasingly critical. Traditional ‘challenge-response’ EDIV methods incur substantial computation and communication costs by indiscriminately verifying all ESs, even though not all ESs may be simultaneously corrupted. A recent Federated Learning (FL)-based framework partially addressed this inefficiency by identifying potentially corrupted ESs early, considering only homogeneous ES activity data. However, due to heterogeneous activity data across diverse ESs, this approach suffers from reduced detection accuracy of potentially corrupted ESs, slower FL convergence, and unclear guidance for subsequent verification rounds, thus limiting the overall reduction in EDIV computation and communication costs. To that end, we propose Federated learning with Adaptive Regularizer-based Edge Data Integrity Verification (FedAR-EDIV), which is an effective FL-based framework integrating an adaptive objective regularization strategy specifically designed to handle heterogeneous data distributions. FedAR-EDIV efficiently identifies potentially corrupted ESs during the FL process, achieves faster convergence, and significantly reduces computation and communication costs in the final EDIV procedure. It achieves up to 16× communication speedup and 9.1× computation cost reduction compared to baseline EDIV methods, and reaches FL-based detection accuracy exceeding 99.78% under heterogeneous conditions using KDD99 activity data. Additionally, FedAR-EDIV incorporates a dynamic reputation mechanism after each EDIV round to strategically guide subsequent verification rounds, ensuring fewer checks for trustworthy ESs and greater scrutiny for suspicious ones, thus further minimizing EDIV-related costs. We provide a theoretical analysis that demonstrates the convergence of FedAR-EDIV during FL training, as well as correctness, efficiency, and security during the EDIV process. Extensive experiments conducted on two different heterogeneous activity datasets validated that FedAR-EDIV substantially outperforms baseline methods in terms of corrupted ES detection accuracy, FL convergence speed, and overall EDIV computation and communication costs.