Fed-Universe: A Semantic-Geometric-Topological-Human (S-G-T-H) Stack for Negotiated Alignment in Federated Systems

Ting Xu, Henry Leung
Proceedings of the The 39th Canadian Conference on Artificial Intelligence, PMLR 318:1219-1223, 2026.

Abstract

Conventional federated learners implicitly optimize for global means, creating a “one-size-fits-all” paradigm that inevitably suppresses the minority under cross-site heterogeneity. To bridge this critical gap, we present Fed-Universe, a generalizable Semantic–Geometric–Topological–Human (S–G–T –H) architecture that transforms passive averaging into an active negotiation for decentralized alignment. The S-layer deploys an edge-capable LLM as a semantic surrogate to translate heterogeneous inputs into standardized patient profile prompts. The G-layer enforces geometric quality assurance via a continuous cosine similarity gate to prevent the mis-rejection of valid minority features. The T-layer executes topological Pareto control, identifying a sensitivity-based knee point ($\lambda$k) to balance multi-objective aggregation without majority degradation. Finally, the H-layer operationalizes a cognitive twin dynamic to mirror the user’s real-time psychological state, dynamically adapting human-computer interaction modes via active intent alignment. To ground this theoretical architecture empirically, we validated the S-G-T core on a binational synthetic clinical network simulation. This proof-of-concept demonstrated a “zero-sum escape”: a minority node (<3% data volume) achieved utility parity (loss reduction from 0.857 to 0.340) alongside dominant hubs. Our Fed-Universe framework is actively being expanded to target distinct scales of alignment failure: institutional silos (Fed-Ultra), societal bias (Fed-Urban), and individual cognitive depletion (Fed-Human).

Cite this Paper

BibTeX
@InProceedings{pmlr-v318-xu26b, title = {Fed-Universe: A Semantic-Geometric-Topological-Human (S-G-T-H) Stack for Negotiated Alignment in Federated Systems}, author = {Xu, Ting and Leung, Henry}, booktitle = {Proceedings of the The 39th Canadian Conference on Artificial Intelligence}, pages = {1219--1223}, year = {2026}, editor = {Bouzar-Benlabiod, Lydia and Leung, Carson}, volume = {318}, series = {Proceedings of Machine Learning Research}, month = {25--29 May}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v318/main/assets/xu26b/xu26b.pdf}, url = {https://proceedings.mlr.press/v318/xu26b.html}, abstract = {Conventional federated learners implicitly optimize for global means, creating a “one-size-fits-all” paradigm that inevitably suppresses the minority under cross-site heterogeneity. To bridge this critical gap, we present Fed-Universe, a generalizable Semantic–Geometric–Topological–Human (S–G–T –H) architecture that transforms passive averaging into an active negotiation for decentralized alignment. The S-layer deploys an edge-capable LLM as a semantic surrogate to translate heterogeneous inputs into standardized patient profile prompts. The G-layer enforces geometric quality assurance via a continuous cosine similarity gate to prevent the mis-rejection of valid minority features. The T-layer executes topological Pareto control, identifying a sensitivity-based knee point ($\lambda$k) to balance multi-objective aggregation without majority degradation. Finally, the H-layer operationalizes a cognitive twin dynamic to mirror the user’s real-time psychological state, dynamically adapting human-computer interaction modes via active intent alignment. To ground this theoretical architecture empirically, we validated the S-G-T core on a binational synthetic clinical network simulation. This proof-of-concept demonstrated a “zero-sum escape”: a minority node (<3% data volume) achieved utility parity (loss reduction from 0.857 to 0.340) alongside dominant hubs. Our Fed-Universe framework is actively being expanded to target distinct scales of alignment failure: institutional silos (Fed-Ultra), societal bias (Fed-Urban), and individual cognitive depletion (Fed-Human).} }
Endnote
%0 Conference Paper %T Fed-Universe: A Semantic-Geometric-Topological-Human (S-G-T-H) Stack for Negotiated Alignment in Federated Systems %A Ting Xu %A Henry Leung %B Proceedings of the The 39th Canadian Conference on Artificial Intelligence %C Proceedings of Machine Learning Research %D 2026 %E Lydia Bouzar-Benlabiod %E Carson Leung %F pmlr-v318-xu26b %I PMLR %P 1219--1223 %U https://proceedings.mlr.press/v318/xu26b.html %V 318 %X Conventional federated learners implicitly optimize for global means, creating a “one-size-fits-all” paradigm that inevitably suppresses the minority under cross-site heterogeneity. To bridge this critical gap, we present Fed-Universe, a generalizable Semantic–Geometric–Topological–Human (S–G–T –H) architecture that transforms passive averaging into an active negotiation for decentralized alignment. The S-layer deploys an edge-capable LLM as a semantic surrogate to translate heterogeneous inputs into standardized patient profile prompts. The G-layer enforces geometric quality assurance via a continuous cosine similarity gate to prevent the mis-rejection of valid minority features. The T-layer executes topological Pareto control, identifying a sensitivity-based knee point ($\lambda$k) to balance multi-objective aggregation without majority degradation. Finally, the H-layer operationalizes a cognitive twin dynamic to mirror the user’s real-time psychological state, dynamically adapting human-computer interaction modes via active intent alignment. To ground this theoretical architecture empirically, we validated the S-G-T core on a binational synthetic clinical network simulation. This proof-of-concept demonstrated a “zero-sum escape”: a minority node (<3% data volume) achieved utility parity (loss reduction from 0.857 to 0.340) alongside dominant hubs. Our Fed-Universe framework is actively being expanded to target distinct scales of alignment failure: institutional silos (Fed-Ultra), societal bias (Fed-Urban), and individual cognitive depletion (Fed-Human).
APA
Xu, T. & Leung, H.. (2026). Fed-Universe: A Semantic-Geometric-Topological-Human (S-G-T-H) Stack for Negotiated Alignment in Federated Systems. Proceedings of the The 39th Canadian Conference on Artificial Intelligence, in Proceedings of Machine Learning Research 318:1219-1223 Available from https://proceedings.mlr.press/v318/xu26b.html.

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