Jog Hiller
March 14th, 2025 03:56
Explore ways blockchain technology and federated learning can rebuild AI development with decentralized privacy-focused governance, enabling large-scale collaboration without compromising data security.
The convergence of federal learning (FL) and blockchain technology sets the stage for a new era of artificial intelligence (AI) development, characterized by decentralized governance and enhanced privacy. According to SEI, this powerful combination allows multiple devices or organizations to work together to train AI models without sharing raw data, maintaining privacy.
Federal Learning and Privacy
Federated Learning is a distributed machine learning approach in which model training occurs on many devices or data silos, eliminating the need for data centralization. This method addresses privacy concerns by allowing data to remain on the local device, thereby preventing data leakage and avoiding reliance on central data holders. This approach is particularly useful for sensitive data, such as personal smartphone information and hospital records, and for sensitive data that can be used for AI training without compromising its confidentiality.
Decentralized AI Governance
The collaborative nature of federated learning results in AI models that are not controlled by a single entity. This raises the issue of governance. Who decides how these models are used and updated? Traditional governance often involves centralized management, which can lead to conflicts of interest and lack of transparency. In contrast, blockchain technology provides a decentralized governance model in which decisions are distributed among stakeholders such as data providers and model users. This approach ensures transparency and accountability as all governance actions are inappropriately recorded on the blockchain.
The role of blockchain in federal learning
Integrating blockchain technology with federated learning translates processes into fully distributed operations. The client sends model updates as transactions to the blockchain where the node’s network is aggregated and maintains a global model state. This method eliminates central servers, reduces the risk of a single point of failure, and increases security through blockchain encryption mechanisms.
High-throughput blockchain
The effectiveness of blockchain-based federation learning is the hinge for high throughput. Large-scale federal learning involves thousands of participants, each frequently updated. Although traditional blockchains suffer from such demands, a high-throughput blockchain that can handle 5 gigaga per second handles the required transaction volume, ensuring real-time model training and efficient incentive mechanisms.
Incentive mechanism
High throughput also promotes sophisticated incentive systems. By using blockchain smart contracts, participants are rewarded for honest contributions and punished for malicious behavior. This economic model promotes continuous, high-quality participation and ensures the integrity of the federal learning process.
Overall, the integration of blockchain and federated learning offers a scalable, democratically managed AI model, paving the way for secure and fair AI development.
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