Decentralized AI: Privacy, Fairness, and the Future of Machine Learning
Nov 1, 2024·
·
2 min read
Dipankar Sarkar
Abstract
As artificial intelligence becomes increasingly central to critical systems, concerns about data privacy, algorithmic fairness, and centralized control have intensified. This talk explores how decentralized approaches—including federated learning, blockchain-based governance, and cryptographic techniques—can address these challenges. Drawing from research in Fed-Focal Loss, DePIN protocols, and privacy-preserving machine learning, I present a vision for AI systems that are simultaneously more capable, more fair, and more respectful of individual rights. The presentation covers technical innovations, real-world applications, and the path forward for building AI infrastructure that serves everyone.
Date
Nov 1, 2024 2:00 PM — 3:30 PM
Event
AI & Web3 Summit 2024
Location
Virtual Conference
Talk Overview
This keynote explores the intersection of decentralized systems and artificial intelligence, addressing fundamental questions about how we build AI that is both powerful and trustworthy.
Key Topics Covered
1. The Centralization Problem
- Why current AI development concentrates power and data
- Privacy risks in centralized machine learning
- The innovation bottleneck of closed systems
2. Federated Learning as a Foundation
- How federated learning preserves privacy while enabling collaboration
- Addressing data imbalance in distributed settings
- Real-world applications in healthcare, mobile devices, and finance
3. Blockchain for AI Governance
- Decentralized model registries and provenance tracking
- Token economics for incentivizing model training and data contribution
- Transparent, auditable AI decision-making
4. Privacy-Preserving Techniques
- Differential privacy in distributed settings
- Secure multi-party computation for collaborative learning
- Zero-knowledge proofs for model verification
5. Building Decentralized AI Infrastructure
- DePIN for distributed compute resources
- Incentive design for sustainable AI networks
- Open challenges and research directions
Target Audience
This talk is designed for:
- AI researchers and practitioners interested in decentralized systems
- Blockchain developers exploring AI applications
- Policy makers concerned with AI governance and privacy
- Anyone interested in the future of ethical, accessible AI
Key Takeaways
Attendees will understand:
- How decentralized approaches can address AI’s trust and fairness challenges
- Practical techniques for building privacy-preserving ML systems
- The economic and governance models that make decentralized AI sustainable
- Current limitations and open research questions in the field
