The current AI industry lacks transparency and trust throughout the lifecycle of AI model development. Additionally, high entry barriers limit the expansion of AI applications across various industries.

To address these challenges, federated learning adopts an approach where models are distributed to local clients for independent training. Only the trained parameters are aggregated to update the global model. This method holds significant potential for reducing the overall cost of developing global models and minimizing the exposure of sensitive personal data, thereby greatly enhancing usability.

However, federated learning also faces its own limitations. Key challenges include the difficulty of recruiting a sufficiently diverse and honest pool of participants for model training and the reliance on centralized servers for certain operational tasks.

FLock.io aims to overcome these limitations by integrating various blockchain elements with federated learning methodologies. Its ultimate goal is to democratize the entire AI model lifecycle—from data collection and model proposal to training and application—paving the way for a more creative and trustworthy AI industry.