RAFALE: Runtime Attestation for Secure and Trustworthy Federated Learning on Edge Devices

Abstract

Federated learning (FL) enables training on edge devices, but update-based defenses may overlook runtime tampering, control-flow manipulation, and microarchitectural attacks. RAFALE strengthens integrity through verifiable runtime evidence collected during local training. Control-Flow Integrity (CFI) evidence and Hardware Performance Counter (HPC) measurements obtained through the normal-world performance-monitoring interface are transferred to an OP-TEE Trusted Application, where they are bound to the training round and model update, digested, and signed for server verification. RAFALE was implemented on Raspberry Pi 3B+ clients and evaluated using convolutional neural networks on MNIST and CIFAR-10. Physical-device trials distinguished benign execution from loss-function omission and indirect-target redirection. At T=2 and K=2, HPC-based detection achieved 83.88% accuracy, 91.94% precision, 76.39% recall, an F1-score of 83.44%, and a 7.60% false positive rate. Combined CFI/HPC monitoring required 29.653s per Raspberry Pi training run. Authentication of one physical-device evidence and model record averaged 1.513ms, and each evidence record occupied 588bytes. A 64-client simulation showed that admitting redirected adversarial updates reduced balanced CIFAR-10 accuracy from 68.59% to 25.49% at 50% malicious participation. These results show that authenticated runtime evidence can distinguish the evaluated execution deviations without revealing local training data.