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2. Distributed artificial intelligence - Wikipedia

   en.wikipedia.org/wiki/Distributed_artificial...

   Distributed artificial intelligence systems were conceived as a group of intelligent entities, called agents, that interacted by cooperation, by coexistence or by competition. DAI is categorized into multi-agent systems and distributed problem solving. [3] In multi-agent systems the main focus is how agents coordinate their knowledge and ...

3. Horovod (machine learning) - Wikipedia

   en.wikipedia.org/wiki/Horovod_(machine_learning)

   Horovod is a free and open-source software framework for distributed deep learning training using TensorFlow, Keras, PyTorch, and Apache MXNet. Horovod is hosted under the Linux Foundation AI (LF AI). [3] Horovod has the goal of improving the speed, scale, and resource allocation when training a machine learning model. [4]

4. Federated learning - Wikipedia

   en.wikipedia.org/wiki/Federated_learning

   Diagram of a Federated Learning protocol with smartphones training a global AI model. Federated learning (also known as collaborative learning) is a machine learning technique focusing on settings in which multiple entities (often referred to as clients) collaboratively train a model while ensuring that their data remains decentralized. [1]

5. Multi-agent system - Wikipedia

   en.wikipedia.org/wiki/Multi-agent_system

   A multi-agent system (MAS or "self-organized system") is a computerized system composed of multiple interacting intelligent agents. [1] Multi-agent systems can solve problems that are difficult or impossible for an individual agent or a monolithic system to solve. [ 2 ]

6. Machine learning - Wikipedia

   en.wikipedia.org/wiki/Machine_learning

   Federated learning is an adapted form of distributed artificial intelligence to training machine learning models that decentralizes the training process, allowing for users' privacy to be maintained by not needing to send their data to a centralized server. This also increases efficiency by decentralizing the training process to many devices.

7. Parallel Intelligence - Wikipedia

   en.wikipedia.org/wiki/Parallel_Intelligence

   In the field of finance, Parallel Intelligence has been employed for stock market prediction, fraud detection, and portfolio management. By integrating human financial expertise with AI algorithms that analyze vast amounts of data, Parallel Intelligence systems can enhance investment decision-making processes and mitigate risks. [3]