Graph Representation-based Model Poisoning on the Heterogeneous Internet of Agents

Internet of Agents (IoA) envisions a unified, agent-centric paradigm where heterogeneous large language model (LLM) agents can interconnect and collaborate at scale. Within this paradigm, federated learning (FL) serves as a key enabler that allows distributed LLM agents to co-train global models without centralizing data. However, the FL-enabled IoA system remains vulnerable to model poisoning attacks, and the prevailing distance and similarity-based defenses become fragile at billion-parameter scale and under heterogeneous data distributions. This paper proposes a graph representation-based model poisoning (GRMP) attack, which passively exploits observed benign local models to construct a parameter correlation graph and extends an adversarial variational graph autoencoder to capture and reshape higher-order dependencies. The GRMP attack synthesizes malicious local models that preserve benign-like statistics while embedding adversarial objectives, remaining elusive to detection at the server. Experiments demonstrate a gradual drop in system accuracy under the proposed attack and the ineffectiveness of the prevailing defense mechanism in detecting the attack, underscoring a severe threat to the ambitious IoA paradigm.

Fundamental Limits of Cooperative Integrated Sensing and Communications over Low-Earth Orbit THz Satellite Channels

Terahertz inter-satellite links enable unprecedented sensing precision for Low Earth Orbit (LEO) constellations, yet face fundamental bounds from hardware impairments, pointing errors, and network interference. We develop a Network Cram\'er-Rao Lower Bound (N-CRLB) framework incorporating dynamic topology, hardware quality factor $\Gamma_{\text{eff}}$, phase noise $\sigma^2_\phi$, and cooperative effects through recursive Fisher Information analysis. Our analysis reveals three key insights: (i) hardware and phase noise create power-independent performance ceilings ($\sigma_{\text{ceiling}} \propto \sqrt{\Gamma_{\text{eff}}}$) and floors ($\sigma_{\text{floor}} \propto \sqrt{\sigma^2_\phi}/f_c$), with power-only scaling saturating above $\text{SNR}_{\text{crit}}=1/\Gamma_{\text{eff}}$; (ii) interference coefficients $\alpha_{\ell m}$ enable opportunistic sensing with demonstrated gains of 5.5~dB under specific conditions (65~dB processing gain, 50~dBi antennas); (iii) measurement correlations from shared timing references, when properly modeled, do not degrade performance and can provide common-mode rejection benefits compared to mismodeled independent-noise baselines. Sub-millimeter ranging requires co-optimized hardware ($\Gamma_{\text{eff}}<0.01$), oscillators ($\sigma^2_\phi<10^{-2}$), and appropriate 3D geometry configurations.