Distribute the computation for better efficiency.
(E.g. computing power,
workload, storage management)
Collaborate with the intent to keep the data of each agent heterogeneous, local, and private. Federated learning can be centralized, decentralized, and semi-centralized.
Efficient allocation and execution of the tasks in the appropriate heterogeneous and dynamic distributed computing devices connected at the edge level.
Predict better for future data. In online settings, data becomes available in sequential order.