Abstract
The current generation of personalization systems is fundamentally constrained by a centralized representational paradigm in which users are reduced to static behavioral embeddings owned and monetized by platform operators. Existing recommender architectures infer preference vectors from fragmented interaction telemetry and optimize engagement through increasingly opaque reinforcement pipelines. Such systems exhibit severe deficiencies in epistemic continuity, user sovereignty, contextual cognition, longitudinal identity fidelity, and privacy-preserving personalization. This paper introduces Federated Cognitive Twins (FCT), a next-generation computational architecture for continuously evolving, cryptographically sovereign, locally anchored cognitive simulations representing human behavioral dynamics, ethical priors, attentional signatures, learning topology, persuasion resistance characteristics, trust propagation structures, and adaptive intent trajectories.
Unlike conventional profiles, personas, or recommendation embeddings, a Federated Cognitive Twin constitutes a persistent cognitive substrate capable of simulating probabilistic human response dynamics under varying informational, emotional, environmental, and temporal contexts. The architecture integrates federated learning, decentralized identity systems, encrypted vector cognition stores, neurosymbolic behavioral modeling, trust-aware reinforcement inference, adaptive attention economics, and zero-knowledge preference negotiation. The core innovation is the inversion of the personalization pipeline: instead of platforms extracting user data into centralized optimization infrastructures, cognitive twins become autonomous negotiation agents operating on behalf of individuals. Models travel to the user-controlled twin; user data never leaves sovereign execution environments.
This paper formalizes the theoretical foundations, distributed systems architecture, cryptographic trust framework, adaptive cognition modeling stack, negotiation engine, memory persistence layer, and economic primitives underlying Federated Cognitive Twins. We further present extensive simulated experiments demonstrating improvements in personalization precision, adversarial persuasion resistance, trust calibration fidelity, contextual recommendation alignment, and privacy guarantees compared to centralized recommender systems. Results indicate that FCT architectures can achieve substantial gains in long-horizon preference coherence while simultaneously reducing data leakage surfaces by multiple orders of magnitude. Finally, the paper proposes an emergent economic framework in which individuals license synthetic preference access, temporary cognition windows, and anonymized audience twin aggregates under programmable consent contracts. \
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Manuel-Devadoss, Johny; Johnson, Joanna; and Johnson, Jayden, "Federated Cognitive Twins", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/10218