Towards Efficient and Reliable Training Assurance of Untrusted Federated Learning Participants under Hardware Non-determinism

Federated learning (FL) is a popular privacy-preserving machine learning paradigm, enabling collaborative training across participants without exposing local data. Since FL loses direct control over participants' training executions, a fundamental requirement is to verify that participants faithfully perform the assigned training tasks. In this paper, we present TrustFL+, an efficient, scalable, and reliable verification scheme that ensures the training correctness of federated learning participants by leveraging both Trusted Execution Environments (TEEs) and GPUs. Essentially, it pushes all local training on high-performance but untrusted GPUs, while the TEE replicates the random parts for tunable levels of assurance. A key challenge is that hardware non-determinism can cause the same floating-point operations to yield different results between GPUs and TEEs, leading to false positives when participants behave honestly. TrustFL+ builds on deterministic training by recording rounding directions of intermediate operations during GPU-side model training and reusing them in TEE-based verification. It especially introduces adaptive rounding precisions to practically control non-determinism while maintaining global model performance in federated learning systems with lots of heterogeneous GPUs and iterative training. We prototype TrustFL+ using a range of NVIDIA GPUs covering multiple hardware architectures, along with Intel SGX, and evaluate its performance across convolutional neural networks and transformer-based networks. The experimental results demonstrate that TrustFL+ delivers up to an order of magnitude speedup compared to naive SGX-based training. Furthermore, all models trained with TrustFL+ on different GPU architectures successfully pass verification within SGX, resulting in 0 false positives.