Neuro-Symbolic Federated Learning with Quantum-Safe Cognitive Twins for Personality-Aware Human-AI Collaboration
The proliferation of AI in collaborative environments underscores the need for systems that intuitively adapt to human personalities and cognitive processes, all while upholding stringent privacy and security standards against emerging quantum threats. This paper proposes an innovative framework that synergizes neuro-symbolic federated learning with quantum-safe cognitive twins to realize personality-aware human-AI collaboration. At its core, neuro-symbolic architectures merge the inductive power of deep neural networks excelling in multimodal feature extraction from text, speech, and biometrics with symbolic reasoning engines that enforce interpretable rules for personality traits, such as the Big Five model (openness, conscientiousness, extraversion, agreeableness, neuroticism).Federated learning facilitates decentralized training across heterogeneous edge devices, aggregating local updates without raw data exchange, thus mitigating privacy risks inherent in centralized paradigms. A key innovation is our weighted aggregation scheme tailored to personality divergence where captures client-specific cognitive profiles. Complementing this, quantum-safe cognitive twin’s virtual replicas of user mental states leverage lattice-based post-quantum cryptography (Kyber-1024) for secure bidirectional synchronization, enabling predictive simulations resilient to harvest-now-decrypt-later attacks.Rigorous evaluations on a diverse dataset comprising 500 participants’ interaction logs, personality assessments, and cognitive benchmarks reveal superior performance: 24.7% improvement in interaction success rate, 18% reduction in edge latency, and zero information leakage under quantum simulations versus baselines (FedAvg, non-symbolic twins). Ablation analyses validate each component’s contribution, while scalability tests on Raspberry Pi clusters affirm deployability. This framework paves the way for empathetic, secure AI in domains like swarm robotics, predictive maintenance, and federated cyber-physical systems, bridging the human-AI cognitive divide.
