Shu Hong

Shu Hong, Lingjie Duan

In federated learning (FL), clients cooperatively train a global model without revealing their raw data but gradients or parameters, while the local information can still be disclosed from local outputs transmitted to the parameter server. With such privacy concerns, a client may overly add artificial noise to his local updates to compromise the global model training, and we prove the selfish noise adding leads to an infinite price of anarchy (PoA). This paper proposes a novel pricing mechanism to regulate privacy-sensitive clients without verifying their parameter updates, unlike existing privacy mechanisms that assume the server's full knowledge of added noise. Without knowing the ground truth, our mechanism reaches the social optimum to best balance the global training error and privacy loss, according to the difference between a client's updated parameter and all clients' average parameter. We also improve the FL convergence bound by refining the aggregation rule at the server to account for different clients' noise variances. Moreover, we extend our pricing scheme to fit incomplete information of clients' privacy sensitivities, ensuring their truthful type reporting and the system's ex-ante budget balance. Simulations show that our pricing scheme greatly improves the system performance especially when clients have diverse privacy sensitivities.

Shu Hong, Yongsheng Mei, Mahdi Imani et al.

Bayesian optimization (BO) is a powerful framework for optimizing expensive black-box objectives, yet extending it to graph-structured domains remains challenging due to the discrete and combinatorial nature of graphs. Existing approaches often rely on either full graph topology-impractical for large or partially observed graphs-or incremental exploration, which can lead to slow convergence. We introduce a scalable framework for global optimization over graphs that employs low-rank spectral representations to build Gaussian process (GP) surrogates from sparse structural observations. The method jointly infers graph structure and node representations through learnable embeddings, enabling efficient global search and principled uncertainty estimation even with limited data. We also provide theoretical analysis establishing conditions for accurate recovery of underlying graph structure under different sampling regimes. Experiments on synthetic and real-world datasets demonstrate that our approach achieves faster convergence and improved optimization performance compared to prior methods.

Zeyu Fang, Shu Hong, Huu Trung Thieu et al.

Open Radio Access Network (O-RAN) enables network control through multi-vendor xApps operating both within and across layers, subnets, and domains, whose concurrent execution can trigger conflicts that are latent during the development phase. Existing conflict management approaches rely heavily on joint-execution data, which is often unavailable in practice. To address this limitation, we formalize a novel problem termed conflict reasoning, which involves identifying conflict-inducing conditions given only marginal datasets from each individual xApp. We propose ZODIAC, a three-stage framework for zero-shot conflict condition inference that comprises uncertainty-aware surrogate model training, trajectory-level diffusion training, and compositional guided denoising for efficient, physics-constrained, and reliable condition search. We derive a theoretical lower confidence bound showing that the compositional reasoning in ZODIAC serves as a principled surrogate for true conflict severity, with the epistemic penalty directly controlling the approximation gap. We evaluate ZODIAC on both the lightweight Mobile-Env platform across all three O-RAN Alliance conflict types (direct, indirect, and implicit) and a realistic NS-O-RAN-Flexric simulator. ZODIAC consistently outperforms baseline condition search methods, achieving over 20% higher True Positive Rate at Top-20, substantially stronger Spearman rank correlation, greater scenario diversity, and competitive computational efficiency. Ablation studies confirm the necessity of each guidance component, with epistemic uncertainty penalties proving essential for filtering spurious conflicts. To the best of our knowledge, ZODIAC is the first framework in O-RAN that enables conflict reasoning from marginal offline data without requiring any joint-execution traces.

Rongqian Chen, Shu Hong, Rifatul Islam et al.

Augmented reality (AR) systems are increasingly deployed in tactical environments, but their reliance on seamless human-computer interaction makes them vulnerable to cognitive attacks that manipulate a user's perception and severely compromise user decision-making. To address this challenge, we introduce the Perception Graph, a novel model designed to reason about human perception within these systems. Our model operates by first mimicking the human process of interpreting key information from an MR environment and then representing the outcomes using a semantically meaningful structure. We demonstrate how the model can compute a quantitative score that reflects the level of perception distortion, providing a robust and measurable method for detecting and analyzing the effects of such cognitive attacks.

Qiufeng Li, Shu Hong, Jian Gao et al. · tsinghua

Recent breakthroughs in AI/ML offer exciting opportunities to revolutionize analog design automation through data-driven approaches. In particular, researchers are increasingly fascinated by harnessing the power of generative AI to automate the discovery of novel analog circuit topologies. Unlocking the full potential of generative AI in these data-driven discoveries requires access to large and diverse datasets.Yet, there is a significant barrier in the analog domain--Analog circuit design is inherently proprietary, involving not only confidential circuit structures but also the underlying commercial semiconductor processes. As a result, current generative AI research is largely confined to individual researchers who construct small, narrowly focused private datasets. This fragmentation severely limits collaborative innovation and impedes progress across the research community. To address these challenges, we propose AnalogFed. AnalogFed enables collaborative topology discovery across decentralized clients (e.g., individual researchers or institutions) without requiring the sharing of raw private data. To make this vision practical, we introduce a suite of techniques tailored to the unique challenges of applying FedL in analog design--from generative model development and data heterogeneity handling to privacy-preserving strategies that ensure both flexibility and security for circuit designers and semiconductor manufacturers. Extensive experiments across varying client counts and dataset sizes demonstrate that AnalogFed achieves performance comparable to centralized baselines--while maintaining strict data privacy. Specifically, the generative AI model within AnalogFed achieves state-of-the-art efficiency and scalability in the design of analog circuit topologies.