Xingyan Shi

Pengchao Han, Xingyan Shi, Jianwei Huang

Knowledge distillation (KD) can enable collaborative learning among distributed clients that have different model architectures and do not share their local data and model parameters with others. Each client updates its local model using the average model output/feature of all client models as the target, known as federated KD. However, existing federated KD methods often do not perform well when clients' local models are trained with heterogeneous local datasets. In this paper, we propose Federated knowledge distillation enabled by Adversarial Learning (FedAL) to address the data heterogeneity among clients. First, to alleviate the local model output divergence across clients caused by data heterogeneity, the server acts as a discriminator to guide clients' local model training to achieve consensus model outputs among clients through a min-max game between clients and the discriminator. Moreover, catastrophic forgetting may happen during the clients' local training and global knowledge transfer due to clients' heterogeneous local data. Towards this challenge, we design the less-forgetting regularization for both local training and global knowledge transfer to guarantee clients' ability to transfer/learn knowledge to/from others. Experimental results show that FedAL and its variants achieve higher accuracy than other federated KD baselines.

Xuesong Wang, Mo Li, Xingyan Shi et al.

Semantic communication enhances transmission efficiency by conveying semantic information rather than raw input symbol sequences. Task-oriented semantic communication is a variant that tries to retains only task-specific information, thus achieving greater bandwidth savings. However, these neural-based communication systems are vulnerable to model inversion attacks, where adversaries try to infer sensitive input information from eavesdropped transmitted data. The key challenge, therefore, lies in preserving privacy while ensuring transmission correctness and robustness. While prior studies typically assume that adversaries aim to fully reconstruct the raw input in task-oriented settings, there exist scenarios where pixel-level metrics such as PSNR or SSIM are low, yet the adversary's outputs still suffice to accomplish the downstream task, indicating leakage of sensitive information. We therefore adopt the attacker's task accuracy as a more appropriate metric for evaluating attack effectiveness. To optimize the gap between the legitimate receiver's accuracy and the adversary's accuracy, we propose DiffSem, a diffusion-aided framework for task-oriented semantic communication. DiffSem integrates a transmitter-side self-noising mechanism that adaptively regulates semantic content while compensating for channel noise, and a receiver-side diffusion U-Net that enhances task performance and can be optionally strengthened by self-referential label embeddings. Our experiments demonstrate that DiffSem enables the legitimate receiver to achieve higher accuracy, thereby validating the superior performance of the proposed framework.