arXiv cs.AI· Ting Xu, Henry Leung·· 12 小时前AI 评分22
Fed-Universe：面向联邦系统协商对齐的 S-G-T-H 语义-几何-拓扑-人类架构
Fed-Universe: A Semantic-Geometric-Topological-Human (S-G-T-H) Stack for Negotiated Alignment in Federated Systems
AI 导读
Fed-Universe 提出 S-G-T-H 四层架构，将联邦学习的被动平均改为主动协商对齐。其 S-G-T 核心在跨国合成临床网络仿真中实现“零和逃逸”：数据量不足 3% 的少数节点损失从 0.857 降至 0.340，与主导节点达到效用持平。该框架正沿 Fed-Ultra 多模态对齐与 Fed-Human 认知意图对齐两个方向扩展。
正文
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 (GQA) via a mapping f: Theta -> Q from the statistical parameter space to a physically interpretable quality domain, employing a cosine similarity gate to retain minority updates based on physical geometry rather than statistical proximity. 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 being expanded along two focused pillars: institutional multimodal alignment (Fed-Ultra) and individual cognitive intent alignment (Fed-Human).
| Comments: | 5 pages, 2 figures. Published in the Proceedings of the 39th Canadian Conference on Artificial Intelligence (Canadian AI 2026), PMLR 318:1219-1223. this https URL |
| Subjects: | Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.34009 [cs.AI] |
| (or arXiv:2610.34009v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.34009 arXiv-issued DOI via DataCite (pending registration) |
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| Journal reference: | Proceedings of Machine Learning Research 318:1219-1223, 2026 |
Submission history
From: Ting Xu [view email]
[v1]
Sun, 25 Oct 2026 02:43:50 UTC (5,247 KB)
来源：arXiv cs.AI · arxiv.org