Description: Local consensus refers to an agreement reached among learning models that operate in a decentralized manner before their updates are sent to a central server. This approach is fundamental in the context of federated learning, where multiple devices or nodes collaborate to train an artificial intelligence model without the need to share sensitive data. Instead of centralizing information, each node trains its own model locally using its data, and then, instead of sending the data itself, it sends only the model updates. This process allows local models to reach a consensus on the parameters of the global model, resulting in a more robust and generalized model. Key features of local consensus include data privacy preservation, reduced bandwidth requirements for communication, and improved training efficiency of the model. Moreover, this approach is particularly relevant in applications where data privacy and security are critical, such as in various sectors including healthcare, finance, and mobile devices. In summary, local consensus is a key component in federated learning, enabling collaboration among multiple entities without compromising individual data privacy.
