Literature Review: Foundations for SoraChain AI

Table of Contents

1. Introduction

2. Foundations of Federated Learning (FL)

3. Blockchain with FL

4. Solving Multi-Stakeholder Coordination in FL

5. Security and Attack Vectors in FL

6. Defense Mechanisms with Blockchain-Based FL

7. Data and FL

8. Federated Learning vs Centralized AI: Accuracy and Efficiency

9. Open Research and Industry Trends

10. Real-World Case Studies

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1. Introduction At SoraChain AI, we are building at the frontier where federated learning (FL) meets blockchain — creating a decentralized, privacy-preserving ecosystem for collaborative AI model training and governance.

This literature review synthesizes the latest research across FL architectures, blockchain integration, security, and decentralized coordination. It demonstrates our alignment with cutting-edge developments while highlighting the unique gaps SoraChain AI is built to fill.

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2. Foundations of Federated Learning References:

• Federated Learning: Tools, Principles, and Future Directions

• Flower Research (Flower.ai)

Key Takeaways for SoraChain AI:

• Aligns with current maturity of FL frameworks.

• SoraChain AI leverages these tools to deploy FL across sectors like healthcare and IoT.

• Tackling participation incentives and scaling remains a strategic priority.

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3. Blockchain for Trustless Coordination in FL References:

• Towards Blockchain-Empowered Federated Learning: Fundamentals, Applications, and Challenges (IEEE, 2023)
• Blockchain for Securing Federated Learning Systems: Enhancing Privacy and Trust (Tarun, 2024)
• IEEE Guide for an Architectural Framework for Blockchain‐Based Federated Machine Learning (2025)

Key Takeaways for SoraChain AI:

• Validates the architectural decision to integrate blockchain.

• EVM-based contracts would allow secure aggregation, auditability, and incentive distribution.

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4. Solving Multi-Stakeholder Coordination in FL References:

• Blockchain for Decentralized Federated Learning: A Systematic Literature Review (ScienceDirect, 2022)
• A Survey on Blockchain for Federated Learning (arXiv, 2021)

Key Takeaways for SoraChain AI:

• Enables institutional collaboration without central trust assumptions.

• On-chain version control and smart contract governance directly address identified gaps.

• Paves path for trustless multi-stakeholder collaboration

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5. Security and Attack Vectors in FL References:

• Federated Learning Security and Privacy: A Comprehensive Survey (Springer, 2024)
• A Blockchain-Integrated Federated Learning Approach for Secure Data Sharing (Li, K., 2024)
• Privacy-preserving in Blockchain-based Federated Learning Systems (Sameera K. M, 2024)

Key Takeaways for SoraChain AI:

• SoraChain AI integrates proactive (e.g., encrypted updates) and reactive (e.g., audit trails) strategies.

• Embeds verifiability into each epoch of federated training.

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6. Defense Mechanisms with Blockchain-Based FL References:

• Mitigating Malicious Attacks in Federated Learning via Confidence-Aware Defense
• DPAD: Data Poisoning Attack Defense Mechanism for Federated Learning Systems

Key Takeaways for SoraChain AI:

• Supports robust, scalable defense in adversarial settings.

• Cryptoeconomics strengthens model integrity over time.

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7. Data, Federated Learning and Small Language Models (SLMs) References:

• Decentralized Federated Prototype Learning Across Heterogeneous Data Distributions
• Efficient Federated Learning Tiny Language Models for Mobile Network Feature Prediction
• Scaling Language Model Size in Cross-Device Federated Learning
• A Survey of Federated Fine-Tuning of LLMs (Yebo Wo, 2025)

Key Takeaways for SoraChain AI:

• Supports deployment of intelligent agents at the edge.

• SLMs align well with privacy, speed, and specialization goals.

• Promotes FL viability even in low-resource settings.

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8. Federated Learning vs Centralized AI: Accuracy and Efficiency References:

• A Comprehensive Experimental Comparison Between Federated and Centralized Learning
• Advances and Open Problems in Federated Learning (Kairouz et al., 2021)
• Communication-Efficient Learning of Deep Networks from Decentralized Data (FedAvg) (McMahan et al., 2017)
• Benchmarking Federated Learning on Real-World Medical Imaging Tasks (TU Delft, 2021)
• Federated Learning for Healthcare Informatics (NIH/PMC, 2021)

Key Takeaways for SoraChain AI:

• Empirically validated: FL can match centralized models while preserving privacy.

• Applications of FL are agnostic across personalized agents and enterprise use-cases.

• Enhancements like update compression and adaptive aggregation will boost SoraChain AI performance.

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9. Open Research and Industry Trends References:

• Towards Federated Learning: An Overview of Methods and Applications (ResearchGate, 2023)
• Federated Learning for Edge Computing: Recent Advances, Challenges, and Future Trends
• Large Language Model FL with Blockchain and Unlearning for Cross-Organizational Collaboration
• Flower.ai Research
• NVIDIA FLARE

Key Takeaways for SoraChain AI:

• Anticipates scalability challenges through compression and governance tooling.

• Positioned to lead with modular, open architectures adapted for edge AI.

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10. Real-World Case Studies

• Eye2Gene: A federated learning framework enabling hospitals to collaboratively train AI models to detect genetic eye diseases without sharing sensitive patient data.
• BloodCounts + Flower: A partnership leveraging federated learning to improve early diagnosis of blood-related conditions globally, enabling data privacy and regulatory compliance.
• Owkin Substra: A production-grade open-source federated learning platform supporting cross-institution medical research without centralized data aggregation.
• Owkin Federated RNA-seq Analysis: Owkin shared its FL tool for secure and collaborative analysis of RNA sequencing data across institutions, preserving privacy while improving biomarker discovery.

  Key Takeaways for SoraChain AI:

  • These case studies validate federated learning’s feasibility in sensitive, high-impact domains and personalized intelligence.

  • SoraChain’s value-add lies in bringing in infrastructure that enables governance, auditability, and incentives for a multi-stakeholder trustless collaboration.

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This document serves as a reference for exploring the architecture, vision, and differentiated approach of SoraChain AI.