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

TL;DR

Fed-Universe introduces a novel S-G-T-H framework that replaces passive averaging in federated learning with active negotiation for alignment across heterogeneous sites. It integrates semantic, geometric, topological, and human cognitive layers to preserve minority data utility and enable real-time intent-aware interaction, achieving utility parity for underrepresented nodes in a binational clinical simulation.

Why It Matters

This work addresses a critical flaw in federated learning—suppression of minority data—by enabling equitable model convergence without centralized coordination. Its integration of human cognitive dynamics and geometric quality assurance makes it highly relevant for privacy-sensitive, real-world applications like healthcare and personalized AI.

Key Findings

• The S-G-T-H stack enables active negotiation for alignment, overcoming the 'one-size-fits-all' limitation of conventional federated learning.
• Geometric quality assurance via cosine similarity gates preserves minority updates based on physical interpretability, not statistical dominance.
• A zero-sum escape was achieved: a minority node with <3% data volume matched the utility of dominant hubs, reducing loss from 0.857 to 0.340.