Chaoyu Zhang

Federated Learning (FL) has gained significant attention for its privacy-preserving capabilities, enabling distributed devices to collaboratively train a global model without sharing raw data. However, its distributed nature forces the central server to blindly trust the local training process and aggregate uncertain model updates, making it susceptible to Byzantine attacks from malicious participants, especially in mission-critical scenarios. Detecting such attacks is challenging due to the diverse knowledge across clients, where variations in model updates may stem from benign factors, such as non-IID data, rather than adversarial behavior. Existing data-driven defenses struggle to distinguish malicious updates from natural variations, leading to high false positive rates and poor filtering performance. To address this challenge, we propose Sentinel, a remote attestation (RA)-based scheme for FL systems that regains client-side transparency and mitigates Byzantine attacks from a system security perspective. Our system employs code instrumentation to track control-flow and monitor critical variables in the local training process. Additionally, we utilize a trusted training recorder within a Trusted Execution Environment (TEE) to generate an attestation report, which is cryptographically signed and securely transmitted to the server. Upon verification, the server ensures that legitimate client training processes remain free from program behavior violation or data manipulation, allowing only trusted model updates to be aggregated into the global model. Experimental results on IoT devices demonstrate that Sentinel ensures the trustworthiness of the local training integrity with low runtime and memory overhead.

Cloud-based infrastructures have become the dominant platform for deploying large models, particularly large language models (LLMs). Fine-tuning and inference are increasingly delegated to cloud providers for simplified deployment and access to proprietary models, yet this creates a fundamental trust gap: although cryptographic and TEE-based verification exist, the scale of modern LLMs renders them prohibitive, leaving clients unable to practically audit these processes. This lack of transparency creates concrete security risks that can silently compromise service integrity. We present AFTUNE, an auditable and verifiable framework that ensures the computation integrity of cloud-based fine-tuning and inference. AFTUNE incorporates a lightweight recording and spot-check mechanism that produces verifiable traces of execution. These traces enable clients to later audit whether the training and inference processes followed the agreed configurations. Our evaluation shows that AFTUNE imposes practical computation overhead while enabling selective and efficient verification, demonstrating that trustworthy model services are achievable in today's cloud environments.