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Federated Learning for Higher Education

Collaborate on AI model training while keeping your institutional data secure. PATHS Engine structures your unstructured data into knowledge graphs, enabling powerful federated learning across institutions without compromising privacy or FERPA compliance.

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How PATHS Enables Federated Learning

Structured data from knowledge graphs powers collaborative AI training

1

Data Structuring

PATHS processes unstructured academic content into structured knowledge graphs

2

Local Training

Each institution trains AI models on their structured data locally

3

Parameter Sharing

Only model updates (not raw data) are shared across institutions

4

Collaborative Improvement

Global model improves through aggregated insights from all participants

Why Federated Learning for Higher Education?

Transform your institution's AI capabilities through secure collaboration

Data Security

Your institutional data never leaves your premises. Only model parameter updates are shared, ensuring FERPA and GDPR compliance.

Cost Efficiency

Distribute AI development costs across institutions. Smaller schools can access powerful AI without overextending resources.

Better Models

AI models trained on diverse institutional data are more accurate, less biased, and better adapted to higher education needs.

Collaboration Applications

Explore pilots we can co-build via PATHS: federated training, domain fine-tuning (LoRA/PEFT), and sourced expert material & human-reviewed synthetic data generation.

A1

Subject-Specific AI Tutors

Federated fine-tuning by department (e.g., Calculus, Chem, CS) with LoRA; tutor aligns to local syllabi.

Add-on: human-reviewed synthetic problem sets to cover edge cases.
A2

Assessment & Feedback

Rubric-aligned short-answer & essay feedback trained on instructor-scored samples.

Privacy-safe synthetic responses reviewed by faculty to balance classes.
A3

Early Risk Prediction

Federated models on LMS & textbook logs to flag at-risk students early without moving raw data.

Synthetic “what-if” cohorts to stress-test fairness.
Propose a Pilot

Example Case Studies & Concrete Outcomes

Realistic pilots your team could run with PATHS—using federated training and human-reviewed synthetic data.

Calc I Tutor — Multi-Campus LoRA (Faculty)

PI: Dr. Maya Chen (Assoc. Prof., Mathematics, West Coast Univ.)
Partners: Bay Valley Community College; Ivy Ridge University
Subject: Derivatives (product/chain rules); limits; error analysis

  • Federated PEFT on a 7B model; PATHS graph tags outcomes (Limits→Derivatives→Chain Rule).
  • 2,400 human-reviewed synthetic “near-miss” steps to balance misconceptions.
  • RAG over approved worksheets; answers must cite graph nodes.

Outcomes (8 weeks)

  • +13.8% rubric score (correctness + scaffolding).
  • 1.07× parity: sections across campuses within target fairness band.
  • -18% time-to-mastery on derivatives module (non-graded pilot).
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Technical Implementation

How PATHS Engine powers federated learning with structured knowledge graphs

PATHS Engine's Role

1

Data Structuring

Processes unstructured educational content into structured knowledge graphs with topics, subtopics, and relationships

2

Graph Database Integration

Stores structured data in Neo4j with human-verified relationships, enabling AI models to learn from connected knowledge

3

Feature Extraction

Converts graph data into feature vectors optimized for federated learning with Flower framework

Federated Learning Process

A

Local Training

Each institution trains AI models on their structured PATHS data locally

B

Parameter Updates

Only model weight adjustments are shared, never raw data

C

Global Aggregation

Central server combines updates to create improved global model

D

Model Deployment

Enhanced model is distributed back to all participating institutions

Governance & Benchmarking

Peer-reviewed, benchmark-driven system ensuring quality and fairness

Example: Community College Cohort

Student Success Predictions ≥80% accuracy
Curriculum Effectiveness ≥10% improvement
Bias & Fairness 0.8-1.25 ratio
LMS Engagement ≥15% increase

Governance Process

1

Peer Review Committee

Rotating members evaluate model performance

2

Benchmark Testing

Models tested against HEI-specific metrics

3

Pilot Deployment

One institution tests before full rollout

4

Continuous Monitoring

Ongoing performance tracking and iteration

Join the Federated Learning Initiative

Partner with institutions that share your mission. Leverage PATHS Engine's structured data processing to participate in collaborative AI development while maintaining complete data privacy and institutional control.

Join the Initiative
or email us directly at ai-blue@udel.edu

Privacy First

FERPA-compliant data processing with zero raw data sharing

Peer Reviewed

Benchmark-driven quality assurance with institutional oversight

Cost Effective

Distributed development costs across participating institutions