Myceliary

A research project exploring anti-capitalist frameworks and patterns in AI/ML

Federated Community AI Networks: Small AI, Big Impact

Distributed AI networks that serve communities, not corporations

The Opportunity

Exploits: AI Centralization Orthodoxy
Their Blind Spot: “AI requires massive centralized compute”
Our Approach: Small, distributed AI serving specific communities

While Big Tech concentrates AI power in massive data centers, communities can build federated networks of smaller AI systems that serve local needs better than any centralized system could. This exploits capitalism’s inability to see value in distributed, community-controlled intelligence.

Why This Works

graph TD
    A[Centralized AI] -->|Controls| B[Single Point of Failure]
    B --> C[Corporate Dependence]
    C --> D[Community Vulnerability]
    
    E[Federated AI] -->|Distributes| F[Community Sovereignty]
    F --> G[Local Control]
    G --> H[Collective Resilience]
    
    style A fill:#f99,stroke:#333,stroke-width:2px
    style E fill:#9f9,stroke:#333,stroke-width:2px

How Federation Creates Resilience

graph LR
    subgraph "Corporate Model"
    C1[Big Tech AI] --> U1[User 1]
    C1 --> U2[User 2]
    C1 --> U3[User 3]
    C1 --> U4[User 4]
    end
    
    subgraph "Federated Model"
    N1[Community AI 1] <--> N2[Community AI 2]
    N2 <--> N3[Community AI 3]
    N3 <--> N4[Community AI 4]
    N1 <--> N4
    
    N1 --> M1[Members]
    N2 --> M2[Members]
    N3 --> M3[Members]
    N4 --> M4[Members]
    end
    
    style C1 fill:#ffcccc,stroke:#ff0000,stroke-width:3px
    style N1 fill:#ccffcc,stroke:#00aa00,stroke-width:2px
    style N2 fill:#ccffcc,stroke:#00aa00,stroke-width:2px
    style N3 fill:#ccffcc,stroke:#00aa00,stroke-width:2px
    style N4 fill:#ccffcc,stroke:#00aa00,stroke-width:2px

Capitalist Blind Spots We Exploit

  1. Scale Economics: They can’t see profit in serving small communities
  2. Centralization Efficiency: They miss benefits of distributed decision-making
  3. Data Control: They can’t imagine AI without data extraction
  4. Market Size: They ignore communities too small to monetize

Real-World Applications

Indigenous Language Preservation

Local Knowledge Networks

Mutual Aid Coordination

Democratic Participation

Implementation Guide

Phase 1: Community Readiness Assessment (Month 1)

Identify Partner Communities

Assess Technical Capacity

Phase 2: Federation Design (Months 2-4)

Governance Framework

Technical Architecture

Phase 3: Pilot Network Development (Months 5-8)

Start Small

Build Infrastructure

Phase 4: Network Growth and Sustainability (Months 9-12)

Organic Expansion

Long-term Sustainability

Technical Architecture

Federation Principles

Network Structure

Community A          Community B          Community C
┌─────────────┐     ┌─────────────┐     ┌─────────────┐
│ Local AI    │     │ Local AI    │     │ Local AI    │
│ Node        │◄────┤ Federation  ├────►│ Node        │
│             │     │ Protocol    │     │             │
├─────────────┤     ├─────────────┤     ├─────────────┤
│ Community   │     │ Shared      │     │ Community   │
│ Data &      │     │ Resources   │     │ Data &      │
│ Models      │     │ (Optional)  │     │ Models      │
└─────────────┘     └─────────────┘     └─────────────┘

Key Technical Components

  1. Local AI Nodes
    • Community-sized language models
    • Local training on community data
    • Edge computing for privacy
    • Community-controlled updates
  2. Federation Protocol
    • Encrypted peer-to-peer communication
    • Selective resource sharing
    • Distributed governance mechanisms
    • Emergency coordination systems
  3. Community Interface
    • Culturally appropriate user experience
    • Multi-generational accessibility
    • Integration with existing community tools
    • Democratic control mechanisms

Success Metrics

What We Measure

What We Don’t Measure

Example Implementation: Indigenous Language Federation

The Challenge

The Federation Solution

Network of Sovereign Language Communities

Participating Communities:

How It Works:

  1. Each community controls its own language AI
  2. Communities can share linguistic techniques (not data)
  3. Collaborative development of preservation tools
  4. Mutual support during crises or transitions

Technical Implementation:

Results After 18 Months:

Resources Needed

Minimal Viable Federation

Scaling Considerations

Getting Started

For Communities

  1. Assess Federation Readiness
    • Strong internal governance?
    • Shared values with potential partners?
    • Technical capacity or allies?
    • Commitment to mutual aid?
  2. Find Network Partners
    • Communities facing similar challenges
    • Complementary skills and resources
    • Geographic or cultural connections
    • Proven track record of cooperation
  3. Start Small
    • Single shared project or need
    • Clear agreements about governance
    • Pilot period with evaluation
    • Build trust before expanding

For Technologists

  1. Understand Community Needs
    • Spend significant time with each community
    • Learn governance structures and values
    • Identify technical allies within communities
    • Respect cultural protocols and knowledge
  2. Design for Sovereignty
    • Communities control their own nodes
    • Federation is opt-in for everything
    • Technical systems reflect community values
    • No backdoors or centralized control
  3. Build for Resilience
    • Systems work during emergencies
    • Graceful degradation when nodes disconnect
    • Community capacity for maintenance
    • Open source with community ownership

Case Studies

Appalachian Mutual Aid Federation

Urban Community Garden Alliance

Artisan Craft Preservation Network

Common Questions

Q: How can small communities afford AI infrastructure? A: Federation allows resource sharing, and community-sized AI is much cheaper than corporate systems.

Q: What prevents this from becoming another surveillance system? A: Community ownership and governance from day one, with technical sovereignty.

Q: How do we handle conflicts between federated communities? A: Democratic processes and the option to disconnect - federation is always voluntary.

Q: Can this really compete with corporate AI capabilities? A: It serves different needs - community sovereignty vs. corporate efficiency.

Join the Movement

Ready to build AI networks that serve communities, not corporations?

“The most powerful AI is not the one with the most data, but the one most accountable to the communities it serves.”