Robust Knowledge Distillation in Distributed LLMs Using Prototype-Constrained Semantic Defense Mechanisms
Keywords:
knowledge distillation, distributed LLMs, prototype learning, semantic defense, backdoor attack, adversarial robustness, federated learning, model compression, system architecture, AI governanceAbstract
The widespread deployment of large language models (LLMs) in distributed environments introduces significant vulnerabilities, particularly when knowledge distillation is employed to compress and transfer capabilities across heterogeneous nodes. Adversarial actors can exploit the distillation process to inject backdoors or corrupt semantic representations, undermining the trustworthiness of the student model. This paper proposes a novel defense framework, termed prototype-constrained semantic defense, that integrates prototype-based representation learning with semantic consistency constraints to fortify knowledge distillation against such attacks. The framework operates by establishing a shared semantic anchor space derived from a small set of clean reference samples, then enforcing that the student model’s internal representations remain within defined prototype neighborhoods during distillation. We analyze the architectural trade-offs introduced by this constraint, including its impact on convergence speed, communication overhead, and model fidelity. Through a system-level discussion, we examine deployment considerations for federated and peer-to-peer LLM architectures, addressing governance mechanisms for auditability, fairness in representation alignment across data silos, and sustainability implications of additional computational overhead. Empirical evaluations on multi-task text classification and generation benchmarks demonstrate that the proposed method reduces backdoor success rates by over 85% while maintaining accuracy within 2% of unconstrained distillation baselines. The paper further explores policy implications for responsible LLM deployment, arguing that prototype-based semantic defenses offer a scalable, interpretable path toward robust distributed intelligence. We conclude with a forward-looking perspective on integrating such mechanisms into standardized LLM governance frameworks.
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