We've previously written about how the legal and financial landscape of AI model training has been a messy one but undoubtedly still a place where VC investment and company interest is surging. There’s clearly no putting the AI genie back in the bottle. So what solutions exist to propel AI forward in legal, ethical ways?
Encrypted Decentralized Federated Learning — Enhanced “Data behind Glass”
By leveraging encrypted techniques and decentralized federated learning frameworks, Encrypted Decentralized Federated Learning (EDFL) offers a solution that balances the need for data access with privacy and copyright concerns. EDFL is an innovative approach to machine learning that can enable collaborative model training while preserving data privacy.
How Does Encrypted Decentralized Federated Learning Work?
1. Encryption
Advanced methods ensure that sensitive data remains encrypted throughout the training process, allowing data owners to maintain control over their information while contributing to model training.
2. Decentralization
Data remains distributed across multiple devices or nodes and each device holds its own data and participates in the training process without sharing raw information with the others.
3. Federated Learning
Model training occurs locally on individual devices using their respective data and aggregate model updates happen across decentralized nodes instead of sending raw data to a central server.
4. Robust Aggregation:
The aggregation process from decentralized locales is conducted in a privacy-preserving manner, allowing nodes to contribute to the training process without compromising data confidentiality.
5. Iterative Training:
Models are trained locally on decentralized data over iterative rounds and model updates are aggregated securely until the desired level of accuracy is achieved for the model as a whole.
EDFL offers a promising solution to the legal and ethical challenges associated with data licensing in the AI industry. By employing a decentralized approach, federated learning, and advanced aggregation techniques, such as secure multi-party computation and differential privacy, EDFL ensures that sensitive data remains protected while enabling collaborative model training across distributed datasets. This approach addresses concerns regarding copyright infringement by allowing AI models to learn from decentralized data sources without directly accessing raw data. Additionally, these approaches can enhance the reliability and accuracy of models by reducing the risk of biased or manipulated outcomes.
As venture capitalists evaluate investment opportunities in AI startups, companies employing EDFL and/or those with robust proprietary, first-party data assets present massive opportunities without the risk of legal complexities.
Those Who Own the Data, Own the Future
We've said it before and we'll say it again... proprietary, first-party data is on track to become solid gold.
Read more about AI regulation across the nation.
