FedAgent-Chain

A Secure Federated and Agentic AI Framework for Multilingual Disability-Inclusive Employment in AI Cities

Syed, Toqeer Ali · Siddiqui, Muhammad Shoaib · Ali Akarma Frontiers in Artificial Intelligence, 2026

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Project Overview

FedAgent-Chain is a unified framework enabling distributed institutions — public employment agencies, universities, rehabilitation centres, employers, and assistive-technology providers — to collaboratively train inclusive employment models without centralising sensitive disability data.

The system integrates five technology pillars into a single trustworthy pipeline:

Pillar	Purpose
Federated Learning	Privacy-preserving distributed model training across 4 heterogeneous regional nodes
Fairness-Aware Aggregation	A λ-penalty mechanism in FedAvg that reduces disparity across disability categories
Permissioned Blockchain	Immutable audit trail for model updates and consent management
Agentic AI Services	5 specialised agents for matching, upskilling, accommodation, multilingual support, and governance
Differential Privacy	Gradient clipping and calibrated noise injection for formal privacy guarantees

The framework achieves competitive federated performance while providing trustworthy orchestration, governance-aware decision support, and blockchain-backed auditability — capabilities absent from standard federated baselines.

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Table of Contents

• Key Contributions
• System Architecture
• Installation
• Quick Start — Reproduce in 3 Steps
• Empirical Results
• Figure Showcase
• Fairness & Heterogeneity Analysis
• Systems Performance
• Qualitative Agentic AI Demonstrations
• Reproducibility & Scientific Hardening
• Repository Structure
• Advanced Workflows
• Limitations & Ethical Considerations
• Paper Writing Resources
• Citation
• License

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Key Contributions

1. 🏗️ Unified trustworthy architecture combining federated learning, permissioned blockchain, and agentic AI for disability-inclusive employment across multilingual, multi-institutional, cross-country settings.
2. ⚖️ Fairness-Aware FedAvg: A formalised fairness penalty (λ) integrated into the federated optimisation objective, with an empirical Pareto frontier characterising the accuracy–fairness tradeoff.
3. 🔗 Permissioned blockchain audit layer: Consent traceability, cryptographic model-update hashing, and smart-contract-based access control — without storing raw disability data on-chain.
4. 🤖 Five specialised agentic AI services: Employment matching, adaptive upskilling, workplace accommodation, multilingual communication, and human-in-the-loop governance.
5. 🔬 Reproducible prototype simulation: A four-node cross-country simulation (Saudi Arabia, United States, China, Europe) with synthetic disability-employment data, validated across 5 independent seeds (42, 123, 2024, 777, 999).
6. 📊 Comprehensive systems profiling: Full runtime breakdown, communication cost analysis, and scalability discussion with analytical complexity bounds.

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System Architecture

┌──────────────────────────────────────────────────────────────┐
│  Layer 1 — Users & Stakeholders                              │
│  Persons with Disabilities · Employers · Vocational Advisors │
└────────────────────────┬─────────────────────────────────────┘
                         │
┌────────────────────────▼─────────────────────────────────────┐
│  Layer 2 — Data Ingestion                                    │
│  Synthetic Dataset · O*NET · ESCO · Regional Disability Data │
└────────────────────────┬─────────────────────────────────────┘
                         │
┌────────────────────────▼─────────────────────────────────────┐
│  Layer 3 — Institutional Nodes (4 Countries)                 │
│  Saudi Arabia │ United States │ China │ Europe               │
│  Local Training · Data Preprocessing · Consent Management    │
└────────────────────────┬─────────────────────────────────────┘
                         │ DP-Protected Model Updates
┌────────────────────────▼─────────────────────────────────────┐
│  Layer 4 — Security & Privacy                                │
│  Differential Privacy (ε,δ) · LayerNorm Stabilisation        │
│  Gradient Clipping (C=1.0) · Noise Multiplier (σ=0.1)       │
└────────────────────────┬─────────────────────────────────────┘
                         │
┌────────────────────────▼─────────────────────────────────────┐
│  Layer 5 — Federated Aggregation                             │
│  Standard FedAvg │ Fairness-Aware FedAvg (λ-penalty)        │
│  Weight formula: ρ_i = 1 + λ · min-group-F1_i               │
└─────────┬────────────────────────────────────────────────────┘
          │                              │ Audit Hashes
┌─────────▼───────────────┐  ┌───────────▼────────────────────┐
│  Layer 7 — Agentic AI   │  │  Layer 6 — Blockchain          │
│  Employment Matching    │  │  Permissioned Ledger           │
│  Upskilling Agent       │  │  SHA-256 Hash Chain            │
│  Accommodation Agent    │  │  Smart Contracts               │
│  Multilingual Agent     │  │  Consent Logger                │
│  Governance Agent       │  │  Audit Trail                   │
└─────────────────────────┘  └────────────────────────────────┘

Agent Descriptions:

Agent	Role
Employment Matching	Scores user-job suitability using skill overlap, accommodation coverage, and language compatibility
Upskilling	Identifies skill gaps and recommends targeted training courses
Accommodation	Recommends workplace adaptations (e.g., screen readers, ergonomic setups) based on disability profiles
Multilingual	Provides cross-lingual communication plans between user and employer language environments
Governance	Flags high-risk recommendations for mandatory human review; enforces policy compliance

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Installation

# Clone and enter the repository
git clone https://github.com/aliakarma/fedagent-chain.git
cd fedagent-chain

# Create isolated environment
python -m venv .venv
source .venv/bin/activate        # Windows: .venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt
pip install -e .

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Quick Start — Reproduce in 3 Steps

Step 1 — Generate Data & Run Experiments

# Generate synthetic disability-employment data
python scripts/generate_synthetic_data.py \
    --config configs/experiment/fedagent_chain_full.yaml --seed 42

# Run FedAgent-Chain simulation (repeat for seeds 123, 2024, 777, 999)
python scripts/run_federated_simulation.py --seed 42

# Run baselines (repeat for all seeds)
python scripts/run_baselines.py --seed 42

Step 2 — Aggregate & Evaluate

# Aggregate all 5 seeds into publication tables
python scripts/aggregate_multi_seed_results.py --seeds 42 123 2024 777 999

# Run full evaluation pipeline (generates confusion matrices)
python scripts/run_evaluation.py --seed 42

# Generate ablation comparison table
python scripts/generate_ablation_table.py

Step 3 — Generate Figures & Verify

# Generate all publication figures
python scripts/generate_figures.py
python scripts/generate_lambda_tradeoff_plot.py
python scripts/generate_system_overhead_plots.py

# Run automated integrity check
python scripts/verify_submission_readiness.py

All outputs are saved to experiments/results/.

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Empirical Results

All metrics are computed from trained model checkpoints evaluated on held-out test sets (stratified 80/20 split per node). Results below are aggregated over 5 independent seeds (42, 123, 2024, 777, 999).

Table 2 — Multi-Seed Model Performance (Mean ± 95% CI)

Method	F1 Mean	F1 Std	95% CI
FedAgent-Chain	0.7207	0.0565	[0.6506, 0.7909]
Standard FedAvg	0.7116	0.0718	[0.6225, 0.8007]
Local Baseline	0.5380	0.2753	[0.1962, 0.8799]
Centralized	0.7115	0.0238	[0.6820, 0.7411]

Source: experiments/results/statistics/table_2_multi_seed_summary.csv

Statistical Significance (Paired t-test, n=5 seeds)

Comparison	Δ F1	t	p	Cohen's d	Sig.
FedAgent-Chain vs Standard FedAvg	+0.0091	0.44	0.679	0.20	No
FedAgent-Chain vs Local Baseline	+0.1827	1.31	0.261	0.59	No
FedAgent-Chain vs Centralized	+0.0092	0.61	0.575	0.27	No

Interpretation: FedAgent-Chain achieves competitive performance with all baselines. The lack of statistical significance at n=5 is expected given the limited seed count — the key contribution is not raw metric superiority, but the integration of fairness, governance, and auditability within a federated paradigm.

Source: experiments/results/statistics/statistical_tests.csv

Table 5 — Agentic AI Services

Agent	Metric	Score
Employment Matching	Mean Confidence	0.6937
Upskilling	Skill Gap Coverage	1.0000
Accommodation	Accommodation Coverage	0.7308
Multilingual	Language Adequacy	0.9690
Governance	High-Risk Detection Rate	0.7333
Governance	False Positive Rate	0.0595

Source: experiments/results/seeds/seed_42/table_5_agent_results.csv

Table 6 — Ablation Study (λ-penalty)

Variant	F1 (Mean)	D_fair (Mean)	Runtime
Full System (λ=0.5)	0.7207	0.1653	86.6s
Standard FedAvg (λ=0)	0.7116	0.1610	86.6s

Interpretation: The λ-penalty acts as a group-regularizer, yielding a modest improvement in predictive stability (F1) across heterogeneous nodes while maintaining comparable aggregate fairness disparity.

Source: experiments/results/table_ablation.csv

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Figure Showcase

The following publication-quality figures are available in paper_figures/:

Figure	Description	File
Convergence (CI)	FL training dynamics with 95% confidence bands across 5 seeds	paper_figures/fl_convergence.pdf
Node F1 Scores	Per-region performance comparison across all methods	paper_figures/node_f1_scores.pdf
Fairness Disparity	D_fair evolution over federated rounds	paper_figures/fairness_disparity.pdf
λ Tradeoff (Pareto)	Accuracy–fairness Pareto frontier across 8 λ values	paper_figures/lambda_tradeoff_ci.pdf
Runtime Breakdown	Stacked bar chart of local training vs. aggregation overhead	paper_figures/runtime_breakdown.pdf
Communication Costs	Cumulative transmission volume over 20 rounds	paper_figures/communication_costs.pdf
Confusion Matrix (FedAgent-Chain)	Classification error analysis for the full system	paper_figures/confusion_matrix_fedagent_chain.pdf
Confusion Matrix (Standard FedAvg)	Classification error analysis for baseline FedAvg	paper_figures/confusion_matrix_standard_fedavg.pdf

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Fairness & Heterogeneity Analysis

Table 3 — Fairness Disparity (D_fair, Mean ± Std across 5 seeds)

Protected Attribute	FedAgent-Chain	Std FedAvg	Local Baseline	Centralized
Disability Category	0.0517 ± 0.0147	0.0428 ± 0.0095	0.0764 ± 0.0546	0.0444 ± 0.0193
Language Group	0.4154 ± 0.0493	0.4115 ± 0.0532	0.3248 ± 0.1430	0.4366 ± 0.0843
Work Mode	0.0145 ± 0.0083	0.0169 ± 0.0233	0.0243 ± 0.0273	0.0111 ± 0.0071
Regional Node	0.1795 ± 0.0160	0.1729 ± 0.0205	0.1357 ± 0.0790	0.1764 ± 0.0253

Source: experiments/results/statistics/table_3_multi_seed_summary.csv

Dataset Distribution & Europe-Node Skew

Node	Total Samples	Suitable (1)	Unsuitable (0)	Balance (%)
Saudi Arabia	12,500	6,753	5,747	54.0%
United States	12,500	7,265	5,235	58.1%
China	12,500	7,601	4,899	60.8%
Europe	12,500	4,759	7,741	38.1%

Finding: The Europe node exhibits a distributional skew (38.1% positive rate vs. ~57% average across other nodes). This heterogeneity is a deliberate design choice to stress-test the fairness-aware aggregator under realistic cross-institutional data imbalance. The global model's lower performance on Europe reflects this distributional shift — not a system deficiency.

Source: experiments/results/class_distribution.csv

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Systems Performance

Table 7 — Systems Overhead

Metric	Value	Description
Avg Local Training Time	16.01s	Per-node computation time (5 epochs)
Avg Aggregation Time	0.0005s	Server-side coordination overhead
Avg Blockchain Logging Time	0.0007s	Hash submission latency
Model Size	513 KB	Payload size per communication round

Source: experiments/results/system_overhead.csv

Scalability Discussion

The FedAgent-Chain architecture exhibits linear communication scalability:

1. Communication: Total volume scales as O(R · K · |W|), where R is rounds, K is nodes, and |W| is model size. With a ~500 KB model, a 100-node deployment would transmit ~100 MB per round — well within modern institutional bandwidth.
2. Computation: Local training is parallelised across nodes. Server-side aggregation is O(K · |W|), which is negligible for K < 1000.
3. Blockchain: The audit trail grows linearly with R · K. In production, a Merkle-tree-based accumulator could further compress these logs.

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Qualitative Agentic AI Demonstrations

FedAgent-Chain uses a multi-agent orchestration layer to provide holistic employment support. Three representative scenarios demonstrate the system in action:

Scenario 1: Arabic-Speaking / Visual Accessibility

• Profile: Visually impaired user, primary language Arabic, seeking a Data Analyst role.
• Agent Action: The Multilingual Agent provides a cross-lingual communication plan, while the Accommodation Agent recommends screen-reader and Braille display integrations.
• Outcome: ✅ Approved (Confidence: 0.78).

Scenario 2: Remote Work & Upskilling

• Profile: Mobility-impaired candidate seeking remote work in Finance.
• Agent Action: The Upskilling Agent identifies a skill gap and recommends targeted training courses.
• Outcome: ✅ Approved (Confidence: 0.60).

Scenario 3: Governance Risk Detection

• Profile: High-risk candidate (multiple disabilities) for a manual labor role with low accessibility score (0.2).
• Agent Action: The Governance Agent detects a mismatch between physical requirements and candidate needs.
• Outcome: 🚩 Flagged for Human Review (Risk Score: 1.0).

Full scenario reports: experiments/results/demos/

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Reproducibility & Scientific Hardening

Reproducibility Statement

FedAgent-Chain is designed for full transparency and reproducibility:

• Multi-Seed Validation: n=5 independent random seeds (42, 123, 2024, 777, 999).
• Deterministic Seeding: All local training and data generation use fixed PyTorch and NumPy seeds.
• Hardware Agnostic: Results are verifiable on standard CPU-based workstations (8 GB+ RAM).
• Experiment Manifest: All hyperparameters, seeds, and runtime metadata are recorded in experiments/manifest.yaml.

Verification Resources

Document	Purpose
docs/reproducibility.md	Step-by-step verification checklist for reviewers
docs/scientific_hardening.md	Threats to validity and ethical considerations
experiments/manifest.yaml	Machine-readable experiment provenance
CITATION.cff	Standardised citation metadata

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Repository Structure

fedagent-chain/
├── configs/                        # Hydra experiment configurations
│   └── experiment/                 # Per-experiment YAML configs
├── src/                            # Core framework source code
│   ├── federated/                  # FedAvg, Fairness-Aware aggregator, DP
│   ├── models/                     # Neural network (MLP + LayerNorm)
│   ├── agents/                     # 5 specialised agentic services
│   ├── blockchain/                 # Permissioned chain, smart contracts
│   ├── data/                       # Dataset loading, schema, preprocessing
│   ├── evaluation/                 # Metrics, fairness computation, audit
│   ├── visualization/              # Plotting utilities
│   └── utils/                      # Helpers, logging, seeding
├── scripts/                        # Entry-point scripts
│   ├── run_federated_simulation.py # Main FL training loop
│   ├── run_evaluation.py           # Evaluation + confusion matrices
│   ├── run_baselines.py            # Local & centralised baselines
│   ├── aggregate_multi_seed_results.py
│   ├── generate_figures.py         # Publication plots
│   ├── generate_ablation_table.py  # λ-ablation comparison
│   ├── generate_system_overhead_plots.py
│   ├── generate_lambda_tradeoff_plot.py
│   ├── generate_agent_demonstrations.py
│   └── verify_submission_readiness.py
├── experiments/                    # Output directory
│   ├── results/                    # CSV tables, plots, statistics
│   │   ├── seeds/                  # Raw per-seed metrics
│   │   ├── plots/                  # Publication PDF figures
│   │   ├── statistics/             # Aggregated t-tests and CIs
│   │   └── demos/                  # Qualitative agent case studies
│   ├── runs/                       # Per-run checkpoints and metrics
│   └── manifest.yaml               # Experiment provenance
├── paper_figures/                  # Consolidated publication PDFs
├── docs/                           # Extended documentation
│   ├── paper_results_inventory.md  # Master artifact catalog
│   ├── reproducibility.md          # Verification checklist
│   └── scientific_hardening.md     # Threats & ethics
├── tests/                          # Test suite (unit/integration/regression)
├── CITATION.cff                    # Citation metadata
├── LICENSE                         # Apache 2.0
└── README.md                       # This file

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Advanced Workflows

Performance Profiling

# Regenerate systems overhead plots and CSVs
python scripts/generate_system_overhead_plots.py

Outputs are saved to experiments/results/plots/runtime_breakdown.pdf and experiments/results/plots/communication_costs.pdf.

Lambda Sweep (Pareto Frontier)

# Run fairness-accuracy tradeoff sweep
python scripts/run_lambda_sweep.py

# Generate Pareto plot with confidence intervals
python scripts/generate_lambda_tradeoff_plot.py

Testing

# Unit tests
pytest tests/unit/ -v

# Integration tests
pytest tests/integration/ -v -m integration --timeout=120

# Regression tests (anchored to paper results)
pytest tests/regression/ -v -m regression --timeout=300

Configuration

Experiments are managed via Hydra. Override any parameter at runtime:

python scripts/run_federated_simulation.py \
    --config configs/experiment/fedagent_chain_full.yaml \
    federated.n_rounds=20 \
    privacy.noise_multiplier=0.1 \
    fairness.lambda_fairness=0.5 \
    --seed 123

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Limitations & Ethical Considerations

Limitations

• Synthetic Data: All results are based on synthetically generated disability-employment data calibrated against WHO and World Bank statistics. Performance on real-world clinical or institutional records may differ.
• Moderate-Scale Evaluation: The current prototype evaluates K=4 regional nodes with n=5 random seeds. Larger-scale deployments may introduce additional heterogeneity and communication challenges.
• Fairness Tradeoffs: The λ-penalty targets disability category as the primary sensitive attribute. Intersectional fairness (e.g., combining disability with age or gender) remains a subject for future work.
• Statistical Power: With n=5 seeds, pairwise comparisons lack sufficient statistical power for formal significance claims at α=0.05. We report effect sizes (Cohen's d) alongside p-values for transparency.

Ethical Considerations

• No real disability data is collected, stored, or processed in this prototype.
• Human oversight is mandatory: The Governance Agent provides recommendations; final employment decisions must always be made by qualified human advisors.
• The system must never automatically reject a person with a disability's employment application without human review.
• Privacy-Utility Tradeoff: Stronger DP noise multipliers improve privacy but can degrade matching accuracy. We recommend calibrating σ based on local regulation (e.g., GDPR, NDMO).

For an extended discussion, see Scientific Hardening & Ethics.

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Paper Writing Resources

Researchers and authors can use the following artifacts to build the main manuscript:

Resource	Description
docs/paper_results_inventory.md	Master catalog of every figure, table, and statistical result
paper_figures/	8 publication-quality PDF figures
experiments/results/demos/	3 qualitative agent case study reports
experiments/results/statistics/	Aggregated CSV tables and statistical tests

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Citation

@article{syed2026fedagentchain,
  title   = {FedAgent-Chain: A Secure Federated and Agentic AI Framework
             for Multilingual Disability-Inclusive Employment in AI Cities},
  author  = {Syed, Toqeer Ali and Siddiqui, Muhammad Shoaib and Ali Akarma},
  journal = {Frontiers in Artificial Intelligence},
  year    = {2026},
}

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License

This project is licensed under the Apache License 2.0.

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For questions, issues, or collaboration inquiries, please open a GitHub Issue.