Decentralized Artificial Intelligence

Decentralized Artificial Intelligence (DAI) aims to overcome limitations of centralized AI by distributing data and computation across multiple entities, enhancing data privacy and security. Current research focuses on federated learning, blockchain-based incentive mechanisms, and novel architectures for efficient and fair model training across diverse networks, including those utilizing homomorphic encryption and Markov blankets for resource optimization. This approach holds significant promise for applications requiring data sovereignty, such as healthcare and smart mobility, while also advancing fundamental research in AI fairness and privacy-preserving collaboration.